Received: 23 August 2021
Revised: 23 March 2022
Accepted: 29 April 2022
DOI: 10.1002/joc.7684
RESEARCH ARTICLE
Assessing the changes of precipitation extremes in
Peninsular Malaysia
Cia Yik Ng1
Sai Hin Lai
| Wan Zurina Wan Jaafar1
1
| Yiwen Mei2
| Faridah Othman1
|
3
| Juneng Liew
1
Department of Civil Engineering, Faculty
of Engineering, University of Malaya,
Kuala Lumpur, Malaysia
2
School for Environment and
Sustainability, University of Michigan,
Ann Arbor, Michigan, USA
3
Center for Earth Sciences and
Environment, Faculty of Science and
Technology, Universiti Kebangsaan
Malaysia, Bangi, Malaysia
Correspondence
Wan Zurina Wan Jaafar, Department of
Civil Engineering, Faculty of Engineering,
University of Malaya, 50603 Kuala
Lumpur, Malaysia.
Email: wzurina@um.edu.my
Funding information
Ministry of Higher Education Malaysia,
Grant/Award Number: FRGS/1/2018/
TK01/UM/02/3
Abstract
The rise of global surface temperature due to warming climate is expected to
increase the intensity and occurrence of extreme precipitation events. Previous
studies in Southeast Asia revealed complex variations in changes of precipitation extremes. This study presents a spatial–temporal analysis on changes of
precipitation extremes in Peninsular Malaysia by utilizing long-term daily
rainfall records at 64 observed stations during 1989–2018. The modified
Mann–Kendall and Sen's slope tests were performed to detect the significance
and magnitude of trends in eight extreme precipitation indices recommended
by the Expert Team on Climate Change Detection and Indices. Statistically significant increasing trends are observed for four of these extreme indices in the
annual assessment. Spatial analysis demonstrates an obvious contrast between
wet and dry regions in patterns of precipitation extremes. Seasonal analysis
reveals the intensity and frequency of wet extremes are enhanced significantly
during the northeast monsoon season. Significant correlations are found
between precipitation extremes and El Niño–Southern Oscillation, particularly
in the northern, eastern and southwest regions. Collectively, the evidence presented suggests that the occurrence of precipitation extremes in Peninsular
Malaysia tends to be more frequent and intense over the year and is closely
associated with the combined effects of tropical monsoon cycles and El Niño–
Southern Oscillation.
KEYWORDS
climate variabilities, El Niño–Southern Oscillation, modified Mann–Kendall test, Monte
Carlo technique, Peninsular Malaysia, precipitation extremes, trend analysis, tropical
monsoon cycles
1 | INTRODUCTION
Global warming can intensify the hydrological cycle
and increase the intensity and occurrence of extreme
precipitation events (Kundzewicz, 2008), leading to the
emergence of more and more climate vulnerable areas.
This poses severe environmental issues to developing
Int J Climatol. 2022;1–24.
countries in Southeast Asia, which are constantly under
the impacts of climate change in the pursuit of sustainable development. The adverse impacts of hydrologic
extremes (e.g., droughts, pluvials and floods) can
severely affect human livelihood and economic development (Diffenbaugh et al., 2017). Therefore, understanding historical changes of precipitation extremes is
wileyonlinelibrary.com/journal/joc
© 2022 Royal Meteorological Society
1
NG ET AL.
2
crucial for the development of more comprehensive and
long-term climate resilience solutions.
Malaysia is a developing country located in Southeast
Asia and the tropical climatic zone. The country receives
more than 2,500 mm of rainfall annually, with most of
the rainfall occurring during the November–March monsoon season. The tropical weather and abundant amount
of rainfall play important roles in promoting the agricultural production and crop yields in this natural
resources-rich country. Nevertheless, the vast amounts of
annual rainfall that are beneficial to agriculture and
hydropower generation can act adversely as catastrophic
precipitation events that occur every year. Peninsular
Malaysia suffered losses from numerous floods in the
past decade, with the most significant occurring during
2014/2015 and 2017 (Baharuddin et al., 2015; Shamshuddin
and Yusoff, 2016; Yahya et al., 2016; Davies, 2017a; 2017b;
2017c; New Straits Times, 2017a; 2017b). To examine the
potential recurrence risks of extreme precipitation events,
the changes of precipitation extremes over Peninsular
Malaysia were explored in numerous studies. Zin
et al. (2010) utilized rainfall records from 10 rain gauge stations to analyze the trends of extreme climate indices from
1971 to 2005; they found positive trends for the intensity
indices but negative trends for the frequency indices.
Suhaila et al. (2010) obtained similar trends for the southwest monsoon season, but they found increasing trends for
both the frequency and intensity indices for the northeast
monsoon season. Besides, Abdul Halim et al. (2014) and
Wong et al. (2018) found significant positive trends during
northeast monsoon, whereas Khan et al. (2019) claimed
that most of the increasing/decreasing trends found in the
previous studies are due to the effect of long-term persistence, which contradicts all the previous findings. Longterm persistence effect due to long duration of wet or dry
periods can cause overestimation of the trend significance,
leading to misinterpretation of the result. The inconsistent
results in these previous studies highlight the needs to
review the extreme patterns in the study area. Moreover,
none of these studies explore the potential driving factors of
the precipitation extremes in Peninsular Malaysia.
Although the reasons behind those changes of precipitation extremes remain unclear, few studies in Southeast
Asia have suggested strong links between the precipitation anomalies and large-scale climate variabilities.
Villafuerte II et al. (2014) found a significant relationship
between El Niño–Southern Oscillation (ENSO) and precipitation extremes over the Philippines, especially during the seasons close to the ENSO peaking phase.
Furthermore, Villafuerte II et al. (2015) also suggested a
potential intensification and increasing occurrence of
precipitation extremes as the global mean temperature
continues to rise. Li et al. (2018) observed similar strong
correlations between precipitation extremes, ENSO and
global mean temperature anomalies in Singapore, but the
signals of local warming are more evident than global
warming. In the eastern part of Indonesia, Lestari
et al. (2016) reported that the interannual variabilities are
strongly modulated by ENSO events and the number of
consecutive dry days tends to increase by more than
2 months during the El Niño phase compared to La Niña.
In addition, Supari et al. (2018) attributed the variations
of wet and dry anomalies in Indonesia to the equatorial
convergence of two anticyclonic (cyclonic) circulations in
response to the anomalous atmosphere–ocean patterns
during El Niño (La Niña) event. Despite the availability
of numerous papers exploring the potential climate
drivers associated with the precipitation extremes in the
Southeast Asia region, there are limited studies in Peninsular Malaysia that examine the seasonal and interannual
variabilities of precipitation extremes in relation to the
influences of global warming, tropical monsoon cycles
and ENSO.
As the knowledge gaps remain large, this study aims
to (a) characterize annual and seasonal changes in precipitation extremes for Peninsular Malaysia during 1989–
2018, (b) examine the associations between precipitation
extremes, global warming and ENSO events, (c) investigate
the anomalous patterns of regional atmosphere–ocean circulations in response to ENSO. A set of eight extreme precipitation indices recommended by the Expert Team on
Climate Change Detection and Indices (ETCCDI) is used in
the investigation. Furthermore, rainfall datasets from
64 non-World Meteorological Organization (WMO) rain
gauge stations are utilized in this study. The findings of this
study provide insights on spatiotemporal variations of precipitation extremes and how monsoon cycles, ENSO and
regional atmosphere–ocean interactions modulate precipitation extremes in Peninsular Malaysia. The manuscript is
organized as follows: section 2 describes the rainfall data,
extreme precipitation indices and evaluation approach used
for the trend analysis. Results are discussed in section 3
while conclusions are written in section 4.
2 | DATA AND M ETHODS
2.1 | Study area
Peninsular Malaysia, also known as West Malaysia
(Figure 1), is a region consisting of 11 states, including
Perlis, Kedah, Pulau Pinang, Perak, Selangor, Negeri
Sembilan, Melaka, Johor, Kelantan, Terengganu and
Pahang and two federal territories, namely Kuala
Lumpur and Putrajaya. Peninsular Malaysia is divided
from East Malaysia by the South China Sea. Peninsular
NG ET AL.
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F I G U R E 1 Spatial distribution
of rain gauge stations over
Peninsular Malaysia. The thick lines
represent the boundaries of climatic
regions. State abbreviations: PLS,
Perlis; PP, Pulau Pinang; KDH,
Kedah; PRK, Perak; KLT, Kelantan;
TRG, Terengganu; PHG, Pahang;
SGR, Selangor; KL, Kuala Lumpur;
NS, Negeri Sembilan; MLK, Melaka;
JHR, Johor
Malaysia has a land area of approximately 132,090 km2,
ranging from 99.5 E to 104.5 E longitude and from 1 N
to 7 N latitude. The climate type of the country is tropical
rainforest, receiving on average over 2,500 mm rainfall
annually. The rainfall distribution is greatly influenced
by four seasons, namely the northeast monsoon (NEM)
season, the southwest monsoon (SWM) season and two
inter-monsoon (IM) seasons in between (Zin et al., 2010).
The NEM season is the wettest season of the year, which
begins from November to March. It often brings torrential rain to Peninsular Malaysia, causing frequent flood
events, especially in the east coast region. The SWM season is the driest season for the whole Peninsular
Malaysia, with relatively small amounts of rainfall from
May to September. The IM seasons occur in April and
October, bringing intense but short-duration convective
rain to the whole region.
2.2 | Data processing
In this study, hourly rainfall records were obtained from
the Malaysia Department of Irrigation and Drainage
(JPS) rain gauge network, which comprises 174 nonWMO stations for the region. The base period used for
this study is from 1989 to 2018 (30-year period), which
covers the greatest number of observed stations in the
study area. Rain gauge stations with higher than 10% of
missing data for the study period were disregarded. Missing values of the remaining stations were filled with the
NG ET AL.
4
nearby good quality stations, prioritizing the stations
with identical elevation. The hourly rainfall data was
adjusted for time zones and accumulated to daily resolution. Then, the daily precipitation time series were examined for homogeneity to rule out the potential impacts from
non-climatic shifts due to station relocation and instrument
maintenance (Li et al., 2014; Mahmood and Jia, 2016). This
was done by using the RHtests_dlyPrcp tool (http://etccdi.
pacificclimate.org/software.shtml), which detects potential
change points based on the penalized maximal t and F test
(Wang et al., 2007; Wang, 2008). The detected change points
were carefully examined with the metadata provided by JPS
for validity (for example relocation of stations in the past).
There are 64 out of 174 observed stations (see Table S1,
Supporting Information) that pass the homogeneity test
and with less than 10% missing data. Spatial distribution of
the 64 rain gauge stations is shown in Figure 1.
The 64 precipitation time series were determined for
outliers (values exceeding either the 75th percentile plus
five times the interquartile range of the series or
200 mmday−1). This was carried out using the ClimPACT2
software package (www.climpact-sci.org). All outliers and
large precipitation values were marked as suspicious and
they were either retained (if matched with historical precipitation events that are documented by JPS) or replaced as
missing values.
2.3 | Extreme precipitation indices
In this study, eight extreme precipitation indices were
selected for the analysis (Table 1). Seven of these indices
were chosen from the core indices list recommended by
ETCCDI (http://etccdi.pacificclimate.org/) and the other
TABLE 1
one is the ratio of extreme to total precipitation (R95pTOT)
that is used to measure the contribution from the precipitation above the 95th percentile (Donat et al., 2013; Baek
et al., 2017). These eight precipitation indices may be categorized into two types as the intensity indices (Rx5day,
SDII, PRCPTOT, R95p and R95pTOT) and the frequency
indices (R20mm, CWD and CDD). The calculations of the
extreme precipitation indices were performed using
ClimPACT2. All indices were calculated for every year on a
seasonal basis.
2.4 | Climate data
To explore the underlying climate drivers of precipitation
extremes over Peninsular Malaysia, global surface temperature anomalies were used as an indicator for global
warming. The global surface temperature anomalies data
was obtained from National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS)
Surface Temperature Analysis version 4 (GISTEMP v4)
dataset (https://data.giss.nasa.gov/gistemp/). The Oceanic
Niño Index was utilized to represent the state of ENSO,
which is available from the National Oceanic and Atmospheric Administration of the United States (NOAA) Climate Prediction Center (CPC) (https://origin.cpc.ncep.noaa.
gov/). The Oceanic Niño Index tracks the running 3-month
mean sea surface temperature anomalies in the Niño3.4
region (120 –170 W longitude; 5 –5 S latitude) and is
widely used as an indicator of ENSO conditions (Supari
et al., 2018; Li et al., 2021; Tan et al., 2021). The onset of an
ENSO episode is identified when the quarterly mean sea
surface temperature departure from the 30-year base period
exceeds 0.5 C in the Niño3.4 region. The ENSO years
Definitions of selected extreme precipitation indices
Index
Name
Definitions
Units
Rx5day
Maximum 5-days precipitation
Annual maximum of 5-days precipitation
mm
PRCPTOT
Annual total wet-day precipitation
Annual total precipitation from wet days
mm
SDII
Simple daily intensity index
Average precipitation amount on wet days
mmday−1
R95p
Total precipitation of very wet days
Annual total precipitation when P > 95th
percentile
mm
R95pTOT
Contribution from very wet days
(R95p/PRCPTOT) × 100%
%
R20mm
Number of very heavy precipitation days
Annual count of days when P ≥ 20 mm
days
CWD
Consecutive wet days
Maximum number of consecutive wet days
days
CDD
Consecutive dry days
Maximum number of consecutive dry days
days
Intensity indices
Frequency indices
Note: A wet/dry day is defined when P ≥/< 1 mm.
Abbreviation: P, daily precipitation.
NG ET AL.
5
identified within the study period are shown in Table S2.
The life cycle of an ENSO event normally evolves from the
developing phase in the summer season (June–July–
August) of the preceding year, to the decaying phase in the
spring season (March–April–May) of the concurrent year
(Juneng and Tangang, 2005). Therefore, the Oceanic Niño
Index values for June–July–August (JJA), September–
October–November (SON), December–January–February
(DJF) and March–April–May (MAM) were applied in this
study to correspond to the evolution period of ENSO
(Juneng and Tangang, 2005; Supari et al., 2018). To understand the regional atmosphere–ocean interaction during
ENSO years, the sea surface temperature anomalies data
available at 2.0 × 2.0 spatial resolution from NOAA
Extended Reconstructed Sea Surface Temperature
version 5 (ERSSTv5) gridded datasets were used in this
study (https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.
html). In addition, the regional moisture flux and vertically integrated moisture divergence were extracted from
the 0.25 × 0.25 ERA5 monthly averaged reanalysis
datasets by the European Centre for Medium-Range
Weather Forecasts (ECMWF) (http://doi.org/10.24381/
cds.f17050d7).
statistic S is then standardized (test statistic Z) to estimate
its significance from the normal cumulative distribution
function. The original variance, Var0(S) and test statistic
Z are calculated as
2.5 | Trend detection and correlation
analysis
where m is the number of ranks of observations. If ri falls
out of the confidence interval, Var0(S) is recalculated as
Var1(S) by
For trend analysis, the rank-based Mann–Kendall test
was applied to detect the presence of statistically significant trends for the extreme precipitation indices. Mann–
Kendall test is a nonparametric statistical test that does
not require any prior distribution assumption for the
data. The null hypothesis of Mann–Kendall test indicates
that there is no trend over time in the series, whereas the
alternative hypothesis indicates that the data is subjected
to increasing or decreasing trend. This study used the
modified Mann–Kendall test (MMK) to account for autocorrelations in the time series (Hamed and Ramachandra
Rao, 1998). The Kendall statistic S, the test statistic of
MMK, is calculated as
S=
X
a ,
i<j ij
ð1Þ
where aij is defined as
8
>
> 1 x i <x j
<
aij =sgn x i −x j = 0 x i =x j :
>
>
: −1
x i >x j
ð2Þ
Under the null hypothesis, the statistic S tends to normality with zero mean. Based on this assumption, the
Var0 ðSÞ =
nðn −1Þð2n +5Þ
,
18
8
S+1
>
>
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
>
>
>
< Var0 ðSÞ
Z= 0
>
>
S−1
>
> pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
>
:
Var0 ðSÞ
ð3Þ
if S<0
if S=0 :
ð4Þ
if S>0
A positive (negative) Z value represents an increasing
(decreasing) trend. If the jZj value is greater than 1.96,
the trend is significant at 5% significance level (rejection
of null hypothesis). To eliminate positive serial correlation in the data, the autocorrelation coefficient of the
ranks of the data, ri, is tested against the null hypothesis
at 95% confidence interval using a 2-tailed test,
ri =
Pm − j
ðx i −x Þ x i + j − x
,
Pm
2
i = 1 ðx i − x Þ
i=1
Var1 ðSÞ =Var0 ðSÞ ×
n
,
ne
ð5Þ
ð6Þ
where n/ne represents the correction to Var0(S) to take
into account the autocorrelation in the data. The term
n/ne is defined as
n−1
X
n
2
×
=1 +
ðn −iÞðn −i −1Þðn −i−2Þr i ,
ne
nðn −1Þðn −2Þ i = 1
ð7Þ
where n is the actual sample size and ne is the effective
sample size to be considered for the autocorrelation
factor in the data. To obtain time series that can represent the whole study area, the regional averages of
each extreme index were calculated using the arithmetic average method with equal weight of all selected
stations.
To quantify the rate of change of the trend, the nonparametric Sen's slope (SS) estimator was used (Sen, 1968).
The rate of change of the trend, Q is defined as
Q =Median
X d −X c
,
d− c
d>c,
ð8Þ
NG ET AL.
6
PRCPTOT
Rx5day
SDII
R95p
R95pTOT
R20mm
CDD
CWD
0.685
0.763
0.901
0.762
0.961
−0.376
0.336
T A B L E 2 Correlation coefficients
of extreme precipitation indices.
Note: Bold values are significant at 0.01 level.
FIGURE 2
Decadal patterns of annual rainfall during 1989–1998, 1999–2008 and 2009–2018
FIGURE 3
Time series for annual rainfall in each state of Peninsular Malaysia during 1989–2018
NG ET AL.
7
where Xd and Xc represent values of the indices at time
d and c. Positive (negative) Qs indicate increasing
(decreasing) trends of the time series. Both MMK and
SS tests were performed using MATLAB (https://www.
mathworks.com/matlabcentral/fileexchange/25533mann-kendall-modified-test).
Monte Carlo technique was adopted to determine the
statistical field significance of the extreme precipitation
indices (Livezey and Chen, 1983). First, MMK tests were
conducted for all observed stations and the total number
of stations with significant trends (N) on each index was
recorded. This step was then repeated for 1,000 times by
randomly shuffling the time series of all stations; the
N values were recorded for all the trials. In this study, the
field significance analysis was performed at 95% confidence level. The index is considered to be field significant
if the actual N value exceeds the N value corresponding
to the 95th percentile over the 1,000 trials. In brief, the
field significance test was used to check the hypothesis
whether the computed trends of extreme precipitation
indices are significant over the whole study area or not.
To justify the indices selection in this study, the correlation analysis was applied to evaluate the correlations of
PRCPTOT to the other indices at 0.01 significance level
(de Lima et al., 2015; Wang et al., 2018). Table 2 shows
that there are statistically significant strong correlations
(>0.6) between the PRCPTOT and Rx5day, SDII, R95p,
R95pTOT and R20mm. The result is in agreement with
the findings from previous study in Peninsular Malaysia,
which suggests that PRCPTOT is well-correlated (>0.5)
with extreme precipitation indices, but has weak correlation with CDD and CWD (Tan et al., 2017).
The teleconnections between extreme precipitation
indices and climate variabilities were examined by using
the Spearman's ρ correlation approach. The Spearman's ρ
correlation test is a nonparametric test used to measure
the strength of associations between the rank values of
two variables, where the value +1 (−1) indicates a perfect
T A B L E 3 Trends (Sen's slope
estimate) in regional mean of all
selected extreme precipitation indices
for Peninsular Malaysia during
1989–2018
positive (negative) correlation. For the correlation between
ENSO and extreme precipitation indices, annual Oceanic
Niño Index was computed by averaging the values of JJA
(0) and SON(0) seasons from the preceding year (usually
referred as year “0”) and D(0)JF(1) and MAM(1) seasons
from the concurrent year (usually referred as year “1”) to
consider the ENSO capacitor effect and delayed response
post peak of the ENSO cycles (Yang et al., 2007; Xie
et al., 2009; 2016; Tan et al., 2021). Field significance of the
correlation analysis between extreme precipitation indices
and climate variabilities were determined by applying the
same Monte Carlo technique.
3 | RESULTS A ND DISCUSSIONS
To understand the rainfall variability of Peninsular
Malaysia, decadal patterns of annual rainfall were analyzed (Figure 2) and the temporal trends were calculated
for the states (Figure 3). Generally, an increasing amount
of annual rainfall is observed for every state of Peninsular
Malaysia, except Johor (Figure 3). From Figure 3, six
states display high increasing rate (>100 mmdecade−1);
they are Kuala Lumpur (175.734 mmdecade−1), Kelantan
Negeri
Sembilan
(143.132 mmdecade−1),
Pulau
Pinang
(178.912
(161.083 mmdecade−1),
−1
−1
mmdecade ), Perak (101.485 mmdecade ) and Terengganu (184.431 mmdecade−1). Among the six states, the
positive trends for Kuala Lumpur, Perak and Terengganu
are statistically significant. This finding is linked to the
intensification of annual rainfall, particularly in the east
coast of Peninsular Malaysia as shown in Figure 2. The
upward trends at the east coast are consistent with the
findings reported by Tan et al. (2017), which was
106.10 mmdecade−1 in the Kelantan River Basin for
1985–2014. The northern states also show positive annual
rainfall trends, ranging from 45.821 to 178.912
mmdecade−1 (Figure 3).
Seasonal trends
Climate indices
−1
Annual trends
NEM
SWM
IM
Rx5day (mmdecade )
11.39
17.25
−1.07
2.21
PRCPTOT (mmdecade−1)
96.90
78.45
15.83
3.91
SDII (mmday−1decade−1)
0.55
0.88
0.48
0.30
59.79
36.19
8.35
2.79
R95pTOT (%decade )
1.24
−1.33
0.28
0.07
R20mm (daydecade−1)
1.42
0.97
0.32
0.21
CDD (daydecade−1)
0.35
−1.34
0.62
0.44
−1
−0.23
−0.27
−0.18
−0.01
−1
R95p (mmdecade )
−1
CWD (daydecade )
Note: Bold values are field significant trends at 95% level.
NG ET AL.
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F I G U R E 4 Spatial distribution of annual trends for the intensity indices over Peninsular Malaysia during 1989–2018. Filled (hollow)
triangle represents stations with statistically significant (nonsignificant) trends
3.1 | What are the annual trends of
precipitation extremes?
3.1.1
| Intensity indices
Table 3 shows the trends of intensity indices in Peninsular Malaysia during 1989–2018. From Table 3, three of
the intensity indices (PRCPTOT, SDII and R95p) show
statistically significant increasing trends. More than half
of the observed stations recorded positive trends for these
indices, which indicates a strong signal of increasing
intensity in precipitation extremes over Peninsular
Malaysia. Figure 4 shows that the spatial distribution of
stations with significant positive trends are concentrated
at the eastern, northern and southwest regions of Peninsular Malaysia. These regions are mainly floodplain areas
with low elevations (Figure 1), which are susceptible to
the impacts of precipitation extremes. Main cities of these
regions have experienced multiple record-setting extreme
precipitation events in the past 10 years, with severe economic losses and casualties (Baharuddin et al., 2015; Lee
and Tuan Resdi, 2016; Shamshuddin and Yusoff, 2016;
Yahya et al., 2016; Malaysiakini, 2017; The Malaysian
Reserve, 2017; Davies, 2017a; 2017b; 2017c; New Straits
NG ET AL.
9
F I G U R E 5 Spatial distribution of annual trends for the frequency indices over Peninsular Malaysia during 1989–2018. Filled (hollow)
triangle represents stations with statistically significant (nonsignificant) trends; hollow circle represents stations with stationary trends
Times, 2017a; 2017b). On the contrary, stations displaying significant decreasing trends for PRCPTOT during the study period are located at the central region or
with higher elevation (Figure 4b). For R95pTOT, the stations with significant increasing trends are primarily distributed along the eastern and southwest coastlines. This
observation suggests that the extreme precipitation indices in these regions are changing more rapidly than
annual rainfall. The increasing contribution to annual
rainfall also indicates that the extreme precipitation
events are getting more extreme over the year.
could be due to the significant decline of vegetation
cover in Kuala Lumpur (Boori et al., 2015; Hua and
Ping, 2018), as Sy and Quesada (2020) suggest that land
use change can affect rainfall formation by (a) decreasing the atmospheric water content (deforestation) and
(b) increasing surface albedo, both of which cause latent
heat flux changes at the deforested areas and thus
reduce rainfall causing conditions. However, anthropogenic land use change impact on consecutive wet days
in the study area need to be evaluated in future detail
analysis.
3.1.2
3.2 | How do precipitation extremes
change for different monsoon seasons?
| Frequency indices
At 95% field significance, R20mm records significant positive trends at 1.42 daydecade−1 (Table 3). The spatial distributions of the observed stations with significant positive
trends (28% for R20mm) are densely located at the southwestern and northern parts of the studied region
(Figure 5a). From Figure 5, decreasing frequency of precipitation extremes is observed for the central and southern regions of Peninsular Malaysia. These findings
simulate that the wet regions are getting wetter while dry
regions are getting drier (Allan et al., 2010; Liu and
Allan, 2013; Wu and Lau, 2016; Schurer et al., 2020).
Another notable change of extreme patterns from Figure 5
is the reduction of consecutive wet days in the capital of
Malaysia, Kuala Lumpur (Station ID: 3217002 and
3217003), despite the positive trend in R20mm. This
Situated in the western part of Southeast Asia, climate in
Peninsular Malaysia is strongly modulated by the monsoon systems. Trends of intensity and frequency indices
are further evaluated in seasonal scale to identify the
change of extreme patterns in different seasons. Table 3
shows that during the NEM season, five of these indices,
namely Rx5day, PRCPTOT, SDII, R95p and R20mm,
exhibit significant increasing trends. R95pTOT shows a
significant decreasing trend at −1.33%decade−1, despite
the significant increasing trend for R95p. The decreasing
ratio of R95p to PRCPTOT indicates that the rainfall
amount on wet days during NEM season is increasing as
a whole. The decreasing contribution from very wet days
to annual rainfall amounts, despite its increasing
10
NG ET AL.
F I G U R E 6 Spatial distribution of seasonal trends for the intensity indices during NEM season. Filled (hollow) triangle represents
stations with statistically significant (nonsignificant) trends
intensity, could also mean that high intensity rainfall
during wet days has become increasingly common. This
result opposes the findings by Abdul Halim et al. (2014)
and Zin et al. (2010) saying that increasing trends were
observed for R95pTOT for most of the stations in Peninsular Malaysia during NEM season. This is likely due to
the differences in study periods and number of rain gauge
stations utilized. From Figures 6 and 7, the significant
decreasing trends for R95pTOT are observed mainly from
stations located in the northern region, which have
shown increases in both intensity and frequency of precipitation extremes. This suggests that the decreasing
trends of R95pTOT do not necessarily indicate reduction
in wet extremes. Thus, the result needs to be interpreted
cautiously. The increasing trends of rainfall can also be
observed from the east coast and southwest regions
(Figures 6 and 7). In contrast, the stations at the southern
region show decreasing trends for both intensity and frequency indices. This demonstrates a notable difference
between the patterns of wet regions and dry regions due
to the monsoon cycles coupling with topographical
heterogeneity.
For the SWM season, most of the stations located at
the southern half of Peninsular Malaysia show significant
increasing trends for extreme indices (Figures 8 and 9).
From Table 3, three of the intensity indices, namely
NG ET AL.
11
F I G U R E 7 Spatial distribution of seasonal trends for the frequency indices during NEM season. Filled (hollow) triangle represents
stations with statistically significant (nonsignificant) trends; hollow circle represents stations with stationary trends
PRCPTOT, SDII and R95pTOT, show significant increasing trend for the whole region, though the magnitudes are
smaller compared to the trends recorded during NEM season. For the IM seasons, the spatial distribution of the stations with significant trends are scattered across the
studied region without clear patterns (Figures 10 and 11),
as the strong local convection system becomes dominant
(Jamaluddin et al., 2018). Now the question here is, what
contributes to the greater magnitudes of trends in the
NEM season in comparison to the SWM season? This
could be explained by the previous studies done on the climatology of the South China Sea during the NEM season.
The weather in the Maritime Continent during the NEM
season is characterized by two synoptic systems—the
northeasterly cold surge and the near-equator cyclonic
system known as the Borneo vortex (Chang et al., 2005a).
Cold surge is the strong equator surge of low-level cold air
driven by the sharp pressure gradient induced by the
southward propagation of the continental Siberian high in
the mid-latitudes (Ding, 1990; Wu and Chan, 1995). The
high vorticity background created by the horizontal
cyclonic shear of the cold surge (Chang et al., 2005b; Ooi
et al., 2011), facilitated by the low-level convergence of the
warm, moist southeasterly flow and the cool northeasterly
surge (Tangang et al., 2008; Koseki et al., 2014) can result
in the development of cyclonic disturbance in the vicinity
of northwestern Borneo. This quasi-stationary mesoscale
cyclonic disturbance, usually referred to as Borneo vortex
(Chang et al., 2003) or cold surge vortex (Chen et al., 2002),
has a shallow vertical structure in general. Occasionally, it
could intensify into a cyclonic system in the strength of a
tropical depression or a tropical storm that may propagate
westward over the South China Sea and cause extreme
rainfall in Peninsular Malaysia (Juneng et al., 2007). Liang
et al. (2021) found that the Borneo vortex-associated rainfall accounts for 15–25% of the total rainfall amount in
Peninsular Malaysia, but its contribution towards the 95th
percentile maximum daily rainfall is relatively greater (20–
25%). Case studies in Peninsular Malaysia and Indonesia
have also suggested a strong connection between the Borneo vortex and the occurrence of extreme precipitation
events in these regions (Juneng et al., 2007; Tangang
et al., 2008; Isnoor et al., 2019). To understand the long-term
changes in the Borneo vortex and the northeasterly cold
surge, Juneng and Tangang (2010) conducted trend analysis
for the time series of seven indices during 1962–2007. Their
results suggest that the frequency of vortex days is significantly increasing as the vortex centers shift northwestward
away from the Borneo land mass associated with stronger
easterly surge wind. The reduced interaction with the land
topography allowed the Borneo vortex system to remain for
a longer period of time in the marine area, resulting in an
increase in vorticity. These synoptic scale changes can
potentially drive more moisture to the eastern Peninsular
Malaysia over the years, thus causing rainfall changes during the NEM season.
To further analyze the trends of precipitation
extremes during NEM season, the time series of each
12
NG ET AL.
F I G U R E 8 Spatial distribution of seasonal trends for the intensity indices during SWM season. Filled (hollow) triangle represents
stations with statistically significant (nonsignificant) trends
index are plotted in Figure 12. Three intensity indices,
namely Rx5day, PRCPTOT and R95p, recorded their
peaks during 2017, which is also the wettest year of the
study period. The high intensity of rainfall caused multiple flood events for Peninsular Malaysia during the NEM
season in 2017 (The Malaysian Reserve, 2017;
Davies, 2017a; 2017b; 2017c; New Straits Times, 2017a;
2017b). From Figure 12c,g, SDII recorded a huge jump
during 2014, as the high number of consecutive dry days
pushes up the ratio of rainfall amount to the number of
wet days. Large fluctuations of trends are observed from
the time series of four intensity indices (Rx5day,
PRCPTOT, SDII and R95p) during 2014–2017
(Figure 12). The primary cause of these interannual variations in precipitation extremes could be due to the
strong ENSO effect during 2015–2016 (Burton
et al., 2018; Ng et al., 2018; Tan et al., 2021). The influence of ENSO on the climate system in Peninsular
Malaysia will be further discussed in section 3.3.
Additionally, the trends of precipitation extremes during NEM season are analyzed according to the climate
regions defined by Bakar et al. (2020) and Lim (1976).
These climatic regions are defined by applying clustering
analysis based on the rainfall distribution in Peninsular
NG ET AL.
Malaysia and the boundaries of each region are shown in
Figure 1. Table 4 shows that the intensity and frequency
of precipitation extremes in the northern, eastern and
southwest regions generally exhibit significant upward
trends, while the exact opposite trends are observed for
the southern region. These findings are in agreement
with the spatial patterns presented in Figures 6 and 7. In
comparison between these regions, the trends in the east
coast have the greatest magnitudes due to the strong
northeasterly monsoon surge during boreal winter.
3.3 | What are the underlying
mechanisms of the precipitation
anomalies?
To explore the climatic driving mechanisms of precipitation anomalies in Peninsular Malaysia, we examine the
correlations between extreme precipitation indices, global
warming and ENSO. Results for the field significance test
in Table 5 reveals that there is no significant correlation
between global surface temperature anomalies and
extreme precipitation indices. Given the Clausius–
Clapeyron scaling has been widely considered as a guide
for quantifying the increase of precipitation extremes
(Trenberth, 1999; Trenberth et al., 2003), however, in
reality it is difficult to identify direct causal linkages
between global warming and precipitation extremes. The
weak correlation between global surface temperature
13
anomalies and extreme precipitation indices is likely due
to the small-scale convective systems as the main mechanisms for rainfall formation in tropical urban regions
(Marzin et al., 2015). Li et al. (2018) has also reported
stronger dependency between local effects and precipitation extremes in Singapore compared to the effect of
global warming.
In comparison to global temperature, Dittus
et al. (2018) found that the sea surface temperature variability could drive a substantial fraction of interannual
variability in precipitation extremes for the tropical
regions. Many modes of climatic variability are coupled
ocean–atmosphere phenomena, such as the ENSO, which
is one of the most important modes of variability that
influences the global atmospheric circulation on interannual timescales (McPhaden et al., 2006). In Peninsular
Malaysia, the Spearman's ρ correlation test shows that
there are strong correlations between ENSO and five of
the extreme precipitation indices (Table 5). Among those
five extreme precipitation indices, dominant negative correlations are observed between ENSO and PRCPTOT,
SDII, R20mm and CWD, except CDD with positive correlation. This result implies that the cooling (warming)
phase of the ENSO event tends to enhance (suppress) wet
extremes in Peninsular Malaysia during the study period.
Similar negative impacts on the precipitation extremes in
Peninsular Malaysia during the warming phase (El Niño)
of ENSO is observed by Amirudin et al. (2020) during
1960–2018. Our findings are generally consistent with
F I G U R E 9 Spatial distribution of seasonal trends for the frequency indices during SWM season. Filled (hollow) triangle represents
stations with statistically significant (nonsignificant) trends; hollow circle represents stations with stationary trends
NG ET AL.
14
Tangang et al. (2017) and also the ENSO associated
anomalous patterns found in the Philippines (Villafuerte
II et al., 2014). Figure 13 demonstrates the spatial distribution of stations correlated with ENSO. For PRCPTOT,
SDII, R20mm and CWD, the stations showing strong
negative correlations with ENSO are mainly distributed
at the northern, southwest and east coast of Peninsular
Malaysia. Similarly, the observed stations showing strong
positive correlations between CDD and ENSO are located
at the same three regions. This spatial pattern of correlations indicates that the changes in intensity as well as the
frequency of precipitation extremes in these three regions
(Figures 4 and 5) are greatly modulated by the ENSO
phenomena.
A previous study by Juneng and Tangang (2005)
suggested that the regional moisture transport in Southeast Asia is modulated by the anomalous regional
atmosphere–ocean coupled interaction, which evolves
according to phases of ENSO in the presence of the
changing monsoonal background. The availability of
atmospheric moisture content is the primary driver of
rainfall, thus the changes of precipitation extremes during ENSO years are likely to be influenced by this
regional atmosphere–ocean coupled interaction as well.
F I G U R E 1 0 Spatial distribution of seasonal trends for the intensity indices during IM season. Filled (hollow) triangle represents
stations with statistically significant (nonsignificant) trends; hollow circle represents stations with stationary trends
NG ET AL.
To investigate the dynamics of moisture flow during
ENSO years, we analyzed the regional sea surface temperature anomalies, atmospheric moisture flux and vertically integrated moisture divergence in the El Niño
(warming) and La Niña (cooling) years. During JJA season in El Niño years, Figure 14a shows that the northeast, southern and southwest regions of Peninsular
Malaysia experienced moisture divergence in response to
both strong westerly flux from the North Indian Ocean
towards the western North Pacific region and strong easterly flux from the eastern Indonesia towards the South
Indian Ocean. Figure 15a demonstrates an anomalous
cooling spot in the eastern Indonesia and generally
warming conditions over the South Indian Ocean, which
create a dipole contrast of sea surface temperature anomalies across the Maritime Continent. This causes the
strong easterly moisture flux over the Maritime Continent which tends to inhibit the convection process in this
area. Unlike the pattern observed during JJA season,
Peninsular Malaysia experiences moisture convergence
as the South China Sea becomes warmer during SON season (Figures 14b and 15b). The moisture convergence can
also be related to an anticyclonic circulation developed at
the South Indian Ocean (Juneng and Tangang, 2005), as
the high atmospheric pressure drives strong southwesterly flux towards the South China Sea (Figure 14b). During boreal winter in El Niño years (DJF season), the
northern hemisphere becomes cooler and the South
Indian Ocean becomes warmer. An anomalously warm
15
condition is observed in the area between equator and
15 S, extended from the South Indian Ocean towards the
Maritime Continent (Figure 15c). At the same time, the
anticyclonic activity during the previous season has also
weakened rapidly (Juneng and Tangang, 2005). The combined effect of dipole sea surface temperature anomalies
pattern and the weakened anticyclonic motion at the
South Indian Ocean causes dry and cool northeasterlies
from the northern hemisphere to travel across the South
China Sea and Peninsular Malaysia towards the South
Indian Ocean. The dry and cool air tends to suppress the
wet extremes over the Peninsular Malaysia, and the
impacts are clearly shown in their trends particularly
during 1992, 1995, 2010, 2015 and 2016 (Figure 12). This
moisture divergence and dry conditions over the Peninsular Malaysia persist through the MAM season in El Niño
years, with a slight sign of weakening at the southern
region.
Unlike the patterns observed in El Niño years, intense
cooling conditions are spotted at the southwestern of
Australia during the JJA season in La Niña years, which
extended northwards towards the southern Sumatra
(Figure 15e). Dipole sea surface temperature anomalies
pattern is formed when this cooling activity is coupled
with the intense warming condition in the western North
Pacific region. This results in strong southwesterlies
along the southern coast of Peninsular Malaysia, which
caused considerable upwelling at the southern half of
Peninsular Malaysia (Figure 14e). During the SON season
F I G U R E 1 1 Spatial distribution of seasonal trends for the frequency indices during IM season. Filled (hollow) triangle represents
stations with statistically significant (nonsignificant) trends; hollow circle represents stations with stationary trends
NG ET AL.
16
F I G U R E 1 2 Time series for (a) Rx5day, (b) PRCPTOT, (c) SDII, (d) R95p, (e) R95pTOT, (f) R20mm, (g) CDD and (h) CWD over
Peninsular Malaysia during NEM season. Dashed line is the linear trend and R is its correlation coefficient
North
Rx5day (mmdecade−1)
−1
PRCPTOT (mmdecade )
−1
−1
SDII (mmday decade )
East
Southwest
South
Central
10.09
36.57
8.08
−14.81
−2.95
97.53
123.29
83.56
−27.45
−26.72
0.73
1.70
0.71
−0.35
0.16
R95p (mmdecade−1)
28.83
66.23
23.97
−21.38
−4.40
R95pTOT (%decade−1)
−3.54
0.61
−1.22
−0.06
0.38
1.61
1.42
1.55
−0.55
−0.56
CDD (daydecade )
−2.10
−0.59
−1.16
0.00
1.50
CWD (daydecade−1)
0.59
0.08
−0.15
−0.86
−1.14
−1
R20mm (daydecade )
−1
T A B L E 4 Trends (Sen's slope
estimate) in regional mean of all
selected extreme precipitation indices
for each region of Peninsular Malaysia
during NEM season
Note: Bold values are field significant trends at 95% level.
in La Niña years, the center of intense cooling point
shifted northwestward closer to the southern Sumatra,
which strengthens the moisture convergence in the entire
Peninsular Malaysia (Figures 14f and 15f). The cooling
condition in the southeastern South Indian Ocean continues during the DJF season in La Niña years, while the
NG ET AL.
17
northern hemisphere begins cooling during boreal winter
(Figure 15g). The cool zone extended towards the south
of Maritime Continent, forming a barrier of high atmospheric pressure which forces the strong northeasterlies
T A B L E 5 The Spearman's ρ correlations between extreme
precipitation indices and their potential driving factors during
1989–2018
Global surface temperature
anomalies
Rx5day
Oceanic Niño
Index
0.01
0.01
PRCPTOT
−0.17
−0.45
SDII
−0.10
−0.29
R95p
−0.09
−0.28
R95pTOT
−0.07
−0.15
R20mm
−0.21
−0.47
CDD
0.05
0.20
CWD
−0.17
−0.26
Note: Bold values are field significant at 95% level.
from the South China Sea to converge at the relatively
warmer region at the northeastern of South Indian Ocean
(Figure 14g). In comparison to the DJF seasons in El
Niño years, moisture convergence is observed along the
east coast of Peninsular Malaysia. This is likely due to the
relatively stronger easterlies from the western North
Pacific region during the La Niña stage, transporting
warm and moist air to the South China Sea which
enhances the wet extremes in the east coast. The influence of La Niña phenomenon on the strengthening of
wet extremes during DJF seasons is noticeable, especially
during 2000, 2009, 2011 and 2017 (Figure 12). During
MAM seasons, the temperature gradient is relatively gentle in the entire Southeast Asia region (Figure 15h). Moisture convergence is observed across the Maritime
Continent including Peninsular Malaysia. From
Figures 14h, the wet anomaly appears to be the result of
convergence of moisture flux moving from the colder
zones at the western North Pacific (top right corner of
Figure 15h), South Indian Ocean and southwestern of
Australia.
F I G U R E 1 3 Spatial distribution of correlations between ENSO index and (a) Rx5day, (b) PRCPTOT, (c) SDII, (d) R95p, (e) R95pTOT,
(f) R20mm, (g) CDD and (h) CWD over Peninsular Malaysia. Filled (hollow) triangle represents stations with statistically significant
(nonsignificant) trends
18
NG ET AL.
F I G U R E 1 4 Regional moisture flux (vectors; kgm−2s−1) and vertically integrated moisture divergence (shaded; ×10−4 kgm−2s−1)
during (a) JJA, (b) SON, (c) DJF and (d) MAM seasons of El Niño and (e) JJA, (f) SON, (g) DJF and (h) MAM seasons of La Niña
NG ET AL.
F I G U R E 1 5 Sea surface temperature anomaly ( C) during (a) JJA, (b) SON, (c) DJF and (d) MAM seasons of El Niño and (e) JJA, (f)
SON, (g) DJF and (h) MAM seasons of La Niña
19
20
4 | C ON C L U S I ON S
This study evaluated the spatial and temporal trends of
eight extreme precipitation indices based on daily rainfall
records from 64 non-WMO rain gauge stations over Peninsular Malaysia in the 1989 to 2018 period. Statistically
significant increasing trends in extreme precipitation
indices including PRCPTOT, SDII, R95p and R20mm are
observed in the annual assessment. Findings of this study
reveal that the occurrence of extreme precipitation events
in Peninsular Malaysia tends to be more frequent and
intense over the year. The increasing trends detected for
R95pTOT also suggest a shift to extreme precipitation
patterns.
The decadal rainfall distribution illustrates the rapid
increase in annual rainfall at the eastern, southwestern
and northwestern parts of Peninsular Malaysia. The findings are in parallel with the occurrence of catastrophic
flood events at these similar regions. The spatial distributions of observed stations with significant increases in precipitation extremes are mainly distributed along the
coastline or floodplain areas, which have relatively higher
annual rainfall, while stations with decreasing trends are
mostly located at the central mountainous region or inland
areas with fewer annual rainfall. The results demonstrate
an obvious contrast of wet regions (coastal) get wetter and
dry regions (inland) get drier (Allan et al., 2010; Liu and
Allan, 2013; Wu and Lau, 2016; Schurer et al., 2020).
The seasonal trend analysis suggests that the intensity
and frequency of precipitation extremes during NEM season
are generally increasing, but lesser contribution from very
wet days to the total precipitation (decreasing R95pTOT) is
observed, which is different from findings from previous
studies. The normalization of the extreme conditions could
be the reasonable explanation, as the increment of
PRCPTOT has outpaced R95p at a much faster rate. Besides,
seasonal analysis also reveals wide variations for trends of
precipitation extremes during different seasons.
The Spearman's ρ correlation test reveals that there is
no direct linkage between global warming and precipitation extremes in Peninsular Malaysia. This is likely due
to the local convective systems as the main mechanisms
for rainfall formation in tropical urban regions. Strong
correlations are observed between those extreme precipitation indices and ENSO in the study area. The observed
stations with significant correlations are spatially distributed at the northern, southwest and east coast regions of
Peninsular Malaysia. This spatial pattern of correlations
indicates that the intensity and frequency of precipitation
extremes in these three regions are greatly influenced by
the ENSO effects. To explore how ENSO effects are
related to the changes of precipitation extremes in Peninsular Malaysia, sea surface temperature anomalies,
NG ET AL.
atmospheric moisture flux and vertically integrated moisture divergence are used to demonstrate the regional
atmosphere–ocean dynamics during different seasons in
El Niño and La Niña years. The variations in the regional
moisture divergence are found to be closely related to the
anomalous sea surface temperature patterns during El
Niño and La Niña years. The wet extremes in Peninsular
Malaysia tend to weaken during the El Niño phase but
strengthen during the La Niña phase. From the evidence
presented in this study, we conclude that the interannual
rainfall variabilities in Peninsular Malaysia are strongly
modulated by the ENSO cycles.
Findings of this study fill the knowledge gaps on
changes of precipitation extremes and their interactions
with monsoon and ENSO cycles in Peninsular Malaysia.
This study also verifies the complexity of spatial–
temporal variability of precipitation extremes in the study
area, which is deteriorating by the combined effects of
large-scale atmospheric circulations, tropical monsoon
cycles and orographic factors. Therefore, as the precipitation extremes show increasing tendency in Peninsular
Malaysia, it is crucial to project and analyze future
changes in precipitation extremes in terms of their intensity and frequency. The other characteristics and dynamics of precipitation extremes such as the changes of
precipitation extremes at different elevation profiles and
the impact of urbanization need to be evaluated in the
near future to achieve better understanding of the underlying mechanisms. Furthermore, other alternative
sources of rainfall data such as high-resolution satellite
or reanalysis products that are proven to have high skills
in rainfall estimation, can be considered in future precipitation extremes estimation (Mei et al., 2014; Beck
et al., 2019).
AUTHOR CONTRIBUTIONS
Cia Yik Ng: Data curation; formal analysis;
investigation; visualization; writing – original draft; writing – review and editing. Wan Zurina Wan Jaafar:
Conceptualization; funding acquisition; methodology;
project administration; supervision; writing – review and
editing. Yiwen Mei: Conceptualization; funding acquisition; methodology; supervision; writing – review and
editing. Faridah Othman: Conceptualization; funding
acquisition; supervision; writing – review and editing. Sai
Hin Lai: Conceptualization; funding acquisition; supervision; writing – review and editing. Juneng Liew: Conceptualization; funding acquisition; supervision; writing
– review and editing.
A C KN O WL ED G EME N T S
This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant
NG ET AL.
Scheme (FRGS/1/2018/TK01/UM/02/3). Y. Mei is thankful to the M-cube program (U064214) and the Water
Theme project (U068408) of University of Michigan for
financial support. We acknowledge the Malaysia Department of Irrigation and Drainage for making the hourly
rainfall data available in this study. We also appreciate
the ECMWF, NASA GISS, NOAA CPC and NOAA Physical Sciences Laboratory for keeping the climate datasets
freely available online.
FUNDING INFORMATION
Ministry of Higher Education Malaysia, Grant Number:
FRGS/1/2018/TK01/UM/02/3.
ORCID
Cia Yik Ng https://orcid.org/0000-0002-2963-5830
Wan Zurina Wan Jaafar https://orcid.org/0000-00026852-237X
Yiwen Mei https://orcid.org/0000-0002-3326-8287
Faridah Othman https://orcid.org/0000-0002-49523676
Sai Hin Lai https://orcid.org/0000-0002-7143-4805
Juneng Liew https://orcid.org/0000-0001-8839-5198
R EF E RE N C E S
Abdul Halim, S., Mohd Daud, Z. and Juneng, L. (2014) Historical
trend of hourly extreme rainfall in Peninsular Malaysia. Theoretical and Applied Climatology, 120, 259–285. https://doi.org/
10.1007/s00704-014-1145-8.
Allan, R.P., Soden, B.J., John, V.O., Ingram, W. and Good, P. (2010)
Current changes in tropical precipitation. Environmental
Research Letters, 5(2), 025205. https://doi.org/10.1088/17489326/5/2/025205.
Amirudin, A.A., Salimun, E., Tangang, F.T., Juneng, L. and
Zuhairi, M. (2020) Differential influences of teleconnections
from the Indian and Pacific Oceans on rainfall variability in
Southeast Asia. Atmosphere, 11(9), 886. https://doi.org/10.3390/
atmos11090886.
Baek, H.-J., Kim, M.-K. and Kwon, W.-T. (2017) Observed shortand long-term changes in summer precipitation over
South Korea and their links to large-scale circulation anomalies. International Journal of Climatology, 37(2), 972–986.
https://doi.org/10.1002/joc.4753.
Baharuddin, K.A., Abdull Wahab, S.F., Rahman, N.A., Nik
Mohamad, N.H., Tuan Kamauzaman, N.A., Md Noh, T.H. and
Abdul Majod, A.Y. (2015) The record-setting flood of 2014 in
Kelantan: challenges and recommendations from an emergency medicine perspective and why the medical campus stood
dry. Malaysian Journal of Medical Sciences, 22(2), 1–7.
Bakar, M.A.A., Ariff, N.M., Jemain, A.A. and Nadzir, M.S.M. (2020)
Cluster analysis of hourly rainfalls using storm indices in Peninsular Malaysia. Journal of Hydrologic Engineering, 25(7), 05020011.
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001942.
Beck, H.E., Pan, M., Roy, T., Weedon, G.P., Pappenberger, F., van
Dijk, A.I.J.M., Huffman, G.J., Adler, R.F. and Wood, E.F. (2019)
Daily evaluation of 26 precipitation datasets using stage-IV
gauge-radar data for the CONUS. Hydrology and Earth System
21
Sciences, 23(1), 207–224. https://doi.org/10.5194/hess-23-2072019.
Boori, M.S., Netzband, M., Choudhary, K. and Voženílek, V. (2015)
Monitoring and modeling of urban sprawl through remote
sensing and GIS in Kuala Lumpur, Malaysia. Ecological Processes, 4(1), 15. https://doi.org/10.1186/s13717-015-0040-2.
Burton, C., Rifai, S. and Malhi, Y. (2018) Inter-comparison and
assessment of gridded climate products over tropical forests
during the 2015/2016 El Niño. Philosophical Transactions of the
Royal Society B: Biological Sciences, 373(1760), 20170406.
https://doi.org/10.1098/rstb.2017.0406.
Chang, C.-P., Harr, P.A. and Chen, H.-J. (2005a) Synoptic disturbances over the equatorial South China Sea and western maritime continent during boreal winter. Monthly Weather Review,
133(3), 489–503. https://doi.org/10.1175/mwr-2868.1.
Chang, C.-P., Liu, C.-H. and Kuo, H.-C. (2003) Typhoon Vamei: an
equatorial tropical cyclone formation. Geophysical Research Letters, 30(3), 1150. https://doi.org/10.1029/2002GL016365.
Chang, C.-P., Wang, Z., McBride, J. and Liu, C.-H. (2005b) Annual
cycle of Southeast Asia—maritime continent rainfall and the
asymmetric monsoon transition. Journal of Climate, 18(2), 287–
301. https://doi.org/10.1175/jcli-3257.1.
Chen, T.-C., Yen, M.-C., Huang, W.-R. and Gallus, W.A. (2002) An
East Asian cold surge: case study. Monthly Weather Review,
130(9), 2271–2290. https://doi.org/10.1175/1520-0493(2002)
130<2271:Aeacsc>2.0.Co;2.
Davies, R. (2017a) Malaysia—25,000 evacuate floods in Terengganu
and Kelantan. Available at: http://floodlist.com/asia/malaysiafloods-terengganu-kelantan-january-2017/.
Davies, R. (2017b) Malaysia—severe storm and floods leave 7 dead,
10,000 displaced. Available at: http://floodlist.com/asia/
malaysia-penang-kedah-floods-november-2017/.
Davies, R. (2017c) Malaysia—thousands still displaced after second
wave of flooding in weeks. Available at: http://floodlist.com/
asia/malaysia-north-january-2017/.
de Lima, M.I.P., Santo, F.E., Ramos, A.M. and Trigo, R.M. (2015)
Trends and correlations in annual extreme precipitation indices
for mainland Portugal, 1941–2007. Theoretical and Applied Climatology, 119(1), 55–75. https://doi.org/10.1007/s00704-0131079-6.
Diffenbaugh, N.S., Singh, D., Mankin, J.S., Horton, D.E., Swain, D.
L., Touma, D., Charland, A., Liu, Y., Haugen, M., Tsiang, M.
and Rajaratnam, B. (2017) Quantifying the influence of global
warming on unprecedented extreme climate events. Proceedings
of the National Academy of Sciences of the United States of
America, 114, 4881–4886.
Ding, Y. (1990) Build-up, air mass transformation and propagation
of Siberian high and its relations to cold surge in East Asia.
Meteorology and Atmospheric Physics, 44(1), 281–292. https://
doi.org/10.1007/BF01026822.
Dittus, A.J., Karoly, D.J., Donat, M.G., Lewis, S.C. and
Alexander, L.V. (2018) Understanding the role of sea surface
temperature-forcing for variability in global temperature and
precipitation extremes. Weather and Climate Extremes, 21, 1–9.
https://doi.org/10.1016/j.wace.2018.06.002.
Donat, M.G., Alexander, L.V., Yang, H., Durre, I., Vose, R.,
Dunn, R.J.H., Willett, K.M., Aguilar, E., Brunet, M., Caesar, J.,
Hewitson, B., Jack, C., Klein Tank, A.M.G., Kruger, A.C.,
Marengo, J., Peterson, T.C., Renom, M., Oria Rojas, C.,
Rusticucci, M., Salinger, J., Elrayah, A.S., Sekele, S.S.,
Srivastava, A.K., Trewin, B., Villarroel, C., Vincent, L.A.,
22
Zhai, P., Zhang, X. and Kitching, S. (2013) Updated analyses of
temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. Journal of
Geophysical Research: Atmospheres, 118(5), 2098–2118. https://
doi.org/10.1002/jgrd.50150.
Hamed, K.H. and Ramachandra Rao, A. (1998) A modified Mann–
Kendall trend test for autocorrelated data. Journal of Hydrology,
204(1), 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X.
Hua, A.K. and Ping, O.W. (2018) The influence of land-use/landcover changes on land surface temperature: a case study of
Kuala Lumpur metropolitan city. European Journal of Remote
Sensing, 51(1), 1049–1069. https://doi.org/10.1080/22797254.
2018.1542976.
Isnoor, K.F.N., Firdianto, P.U. and Rejeki, H.A. (2019) The activity
of Borneo vortex as a trigger for extreme rain in West Borneo
(case study: 24th–25th January 2017). IOP Conference Series:
Earth and Environmental Science, 303(1), 012061. https://doi.
org/10.1088/1755-1315/303/1/012061.
Jamaluddin, A.F., Tangang, F.T., Chung, J.X., Juneng, L., Sasaki, H. and
Takayabu, I. (2018) Investigating the mechanisms of diurnal rainfall
variability over Peninsular Malaysia using the non-hydrostatic
regional climate model. Meteorology and Atmospheric Physics,
130(6), 611–633. https://doi.org/10.1007/s00703-017-0541-x.
Juneng, L. and Tangang, F.T. (2005) Evolution of ENSO-related
rainfall anomalies in Southeast Asia region and its relationship
with atmosphere–ocean variations in Indo-Pacific sector. Climate Dynamics, 25(4), 337–350. https://doi.org/10.1175/10.
1007/s00382-005-0031-6.
Juneng, L. and Tangang, F.T. (2010) Long-term trends of winter
monsoon synoptic circulations over the maritime continent:
1962–2007. Atmospheric Science Letters, 11(3), 199–203. https://
doi.org/10.1002/asl.272.
Juneng, L., Tangang, F.T. and Reason, C.J.C. (2007) Numerical case
study of an extreme rainfall event during 9–11 December 2004
over the east coast of Peninsular Malaysia. Meteorology and
Atmospheric Physics, 98(1), 81–98. https://doi.org/10.1007/
s00703-006-0236-1.
Khan, N., Pour, S.H., Shahid, S., Ismail, T., Ahmed, K.,
Chung, E.-S., Nawaz, N. and Wang, X. (2019) Spatial distribution of secular trends in rainfall indices of Peninsular Malaysia
in the presence of long-term persistence. Meteorological Applications, 26(4), 655–670. https://doi.org/10.1002/met.1792.
Koseki, S., Koh, T.Y. and Teo, C.K. (2014) Borneo vortex and mesoscale convective rainfall. Atmospheric Chemistry and Physics,
14(9), 4539–4562. https://doi.org/10.5194/acp-14-4539-2014.
Kundzewicz, Z.W. (2008) Climate change impacts on the hydrological cycle. Ecohydrology and Hydrobiology, 8(2), 195–203.
https://doi.org/10.2478/v10104-009-0015-y.
Lee, W.-K. and Tuan Resdi, T.A. (2016) Simultaneous hydrological
prediction at multiple gauging stations using the NARX network for Kemaman catchment, Terengganu, Malaysia. Hydrological Sciences Journal, 61(16), 2930–2945. https://doi.org/10.
1080/02626667.2016.1174333.
Lestari, S., Hamada, J.-I., Syamsudin, F., Sunaryo, M.J. and
Yamanaka, M.D. (2016) ENSO influences on rainfall extremes
around Sulawesi and Maluku Islands in the eastern Indonesian
Maritime Continent. Scientific Online Letters on the Atmosphere,
12, 37–41. https://doi.org/10.2151/sola.2016-008.
NG ET AL.
Li, X., Wang, X. and Babovic, V. (2018) Analysis of variability and
trends of precipitation extremes in Singapore during 1980–
2013. International Journal of Climatology, 38(1), 125–141.
https://doi.org/10.1002/joc.5165.
Li, X., Zhang, K., Gu, P., Feng, H., Yin, Y., Chen, W. and Cheng, B.
(2021) Changes in precipitation extremes in the Yangtze River
basin during 1960–2019 and the association with global
warming, ENSO, and local effects. Science of the Total Environment, 760, 144244. https://doi.org/10.1016/j.scitotenv.2020.
144244.
Li, Z., Yan, Z.W., Cao, L.J. and Jones, P. (2014) Adjusting inhomogeneous daily temperature variability using wavelet analysis.
International Journal of Climatology, 34(4), 1196–1207. https://
doi.org/10.1002/joc.3756.
Liang, J., Catto, J.L., Hawcroft, M., Hodges, K.I., Tan, M.L. and
Haywood, J.M. (2021) Climatology of Borneo vortices in the
HadGEM3-GC3.1 general circulation model. Journal of Climate, 34(9), 3401–3419. https://doi.org/10.1175/jcli-d-200604.1.
Lim, J.T. (1976) Rainfall minimum in Peninsular Malaysia during
the northeast monsoon. Monthly Weather Review, 104(1), 96–99.
https://doi.org/10.1175/1520-0493(1976)104<0096:Rmipmd>2.
0.Co;2.
Liu, C. and Allan, R.P. (2013) Observed and simulated precipitation
responses in wet and dry regions 1850–2100. Environmental
Research Letters, 8(3), 034002. https://doi.org/10.1088/17489326/8/3/034002.
Livezey, R.E. and Chen, W.Y. (1983) Statistical field significance
and its determination by Monte Carlo techniques. Monthly
Weather Review, 111(1), 46–59. https://doi.org/10.1175/15200493(1983)111<0046:Sfsaid>2.0.Co;2.
Mahmood, R. and Jia, S.F. (2016) Quality control and homogenization of daily meteorological data in the trans-boundary region
of the Jhelum River basin. Journal of Geographical Sciences,
26(12), 1661–1674. https://doi.org/10.1007/s11442-016-1351-7.
Malaysiakini. (2017) KL hit by flash floods following afternoon downpour. Available at: https://www.malaysiakini.com/news/400084/.
Marzin, C., Rahmat, R., Bernie, D., Bricheno, L., Buonomo, E.,
Calvert, D., Cannaby, H., Chan, S., Chattopadhyay, M.,
Cheong, W.-K., Hassim, M.E., Gohar, L., Golding, N.,
Gordon, C., Gregory, J., Hein, D., Hines, A., Howard, T.,
Janes, T., Jones, R., Kendon, E., Krijnen, J., Lee, S.-Y.,
Lim, S.-Y., Lo, C.F., Lowe, J., Martin, G., McBeath, K.,
McInnes, K., McSweeney, C., Mizielinski, M., Murphy, J.,
O'Neill, C., Palmer, M., Redmond, G., Roberts, C., Sahany, S.,
Sanderson, M., Scannel, C., Sexton, D., Slingo, F.S.J., Sun, X.,
Tinker, J., Tucker, S., Wang, C., Webster, S., Wilson, S.,
Wood, R., Zhang, S. (2015) Singapore's second national climate
change study—phase 1. Available at: http://ccrs.weather.gov.sg/
Publications-Second-National-Climate-Change-Study-ScienceReports/.
McPhaden, M.J., Zebiak, S.E. and Glantz, M.H. (2006) ENSO as an
integrating concept in earth science. Science, 314(5806), 1740–
1745. https://doi.org/10.1126/science.1132588.
Mei, Y., Anagnostou, E.N., Nikolopoulos, E.I. and Borga, M. (2014)
Error analysis of satellite precipitation products in mountainous basins. Journal of Hydrometeorology, 15(5), 1778–1793.
https://doi.org/10.1175/jhm-d-13-0194.1.
NG ET AL.
New Straits Times. (2017a) Floods cause havoc in Penang, Kedah
and Perak. Available at: https://www.nst.com.my/news/nation/
2017/11/299330/floods-cause-havoc-penang-kedah-and-perak/.
New Straits Times. (2017b) National flood update: Pahang worst
affected, Johor struggles to cope. Available at: https://www.nst.
com.my/news/2017/01/207656/national-flood-update-pahangworst-affected-johor-struggles-cope/.
Ng, C.K.-C., Goh, C.-H., Lin, J.-C., Tan, M.-S., Bong, W.,
Yong, C.-S., Chong, J.-Y., Ooi, P.A.-C., Wong, W.-L. and
Khoo, G. (2018) Water quality variation during a strong El Niño
event in 2016: a case study in Kampar River, Malaysia. Environmental Monitoring and Assessment, 190(7), 402. https://doi.org/
10.1007/s10661-018-6784-2.
Ooi, S.H., Samah, A.A. and Braesicke, P. (2011) A case study of the
Borneo vortex genesis and its interactions with the global circulation. Journal of Geophysical Research: Atmospheres, 116(D21),
21116. https://doi.org/10.1029/2011JD015991.
Schurer, A.P., Ballinger, A.P., Friedman, A.R. and Hegerl, G.C.
(2020) Human influence strengthens the contrast between tropical wet and dry regions. Environmental Research Letters,
15(10), 104026. https://doi.org/10.1088/1748-9326/ab83ab.
Sen, P.K. (1968) Estimates of the regression coefficient based on Kendall's
tau. Journal of the American Statistical Association, 63(324), 1379–
1389. https://doi.org/10.1080/01621459.1968.10480934.
Shamshuddin, J. and Yusoff, M. (2016) Effects of December 2014
great flood on the physico-chemical properties of the soils in the
Kelantan Plains, Malaysia. Journal of Water Resource and Protection, 8, 263–276. https://doi.org/10.4236/jwarp.2016.82023.
Suhaila, J., Deni, S.M., Wan Zin, W.Z. and Jemain, A.A. (2010) Spatial patterns and trends of daily rainfall regime in peninsular
Malaysia during the southwest and northeast monsoons: 1975–
2004. Meteorology and Atmospheric Physics, 110(1), 1–18.
https://doi.org/10.1007/s00703-010-0108-6.
Supari, T., Salimun, F.T., Aldrian, E., Sopaheluwakan, E. and
Juneng, L. (2018) ENSO modulation of seasonal rainfall and
extremes in Indonesia. Climate Dynamics, 51(7), 2559–2580.
https://doi.org/10.1007/s00382-017-4028-8.
Sy, S. and Quesada, B. (2020) Anthropogenic land cover change
impact on climate extremes during the 21st century. Environmental Research Letters, 15(3), 034002. https://doi.org/10.1088/
1748-9326/ab702c.
Tan, M.L., Ibrahim, A.L., Cracknell, A.P. and Yusop, Z. (2017)
Changes in precipitation extremes over the Kelantan River
basin, Malaysia. International Journal of Climatology, 37(10),
3780–3797. https://doi.org/10.1002/joc.4952.
Tan, M.L., Juneng, L., Tangang, F.T., Chung, J.X. and Radin
Firdaus, R.B. (2021) Changes in temperature extremes and their
relationship with ENSO in Malaysia from 1985 to 2018. International Journal of Climatology, 41(S1), E2564–E2580. https://doi.
org/10.1002/joc.6864.
Tangang, F.T., Farzanmanesh, R., Mirzaei, A., Supari, S.E.,
Jamaluddin, A.F. and Juneng, L. (2017) Characteristics of precipitation extremes in Malaysia associated with El Niño and La
Niña events. International Journal of Climatology, 37(S1), 696–
716. https://doi.org/10.1002/joc.5032.
Tangang, F.T., Juneng, L., Salimun, E., Vinayachandran, P.N.,
Seng, Y.K., Reason, C.J.C., Behera, S.K. and Yasunari, T. (2008)
On the roles of the northeast cold surge, the Borneo vortex, the
Madden–Julian Oscillation, and the Indian Ocean Dipole
23
during the extreme 2006/2007 flood in southern Peninsular
Malaysia. Geophysical Research Letters, 35(14), L14S07. https://
doi.org/10.1029/2008GL033429.
The Malaysian Reserve. (2017) Floods worsen nationwide, KL and
Johor hit by flash floods. Available at: https://
themalaysianreserve.com/2017/03/31/floods-worsen-nationwidekl-and-johor-hit-by-flash-floods/.
Trenberth, K.E. (1999) Conceptual framework for changes of
extremes of the hydrological cycle with climate change. Climatic Change, 42(1), 327–339. https://doi.org/10.1023/A:
1005488920935.
Trenberth, K.E., Dai, A., Rasmussen, R.M. and Parsons, D.B. (2003)
The changing character of precipitation. Bulletin of the American Meteorological Society, 84(9), 1205–1218. https://doi.org/10.
1175/bams-84-9-1205.
Villafuerte, M.Q., II, Matsumoto, J., Akasaka, I., Takahashi, H.G.,
Kubota, H. and Cinco, T.A. (2014) Long-term trends and variability of rainfall extremes in The Philippines. Atmospheric
Research, 137, 1–13. https://doi.org/10.1016/j.atmosres.2013.
09.021.
Villafuerte, M.Q., II, Matsumoto, J. and Kubota, H. (2015) Changes
in extreme rainfall in The Philippines (1911–2010) linked to
global mean temperature and ENSO. International Journal of
Climatology, 35(8), 2033–2044. https://doi.org/10.1002/joc.
4105.
Wang, C.-Y., Xie, S.-P. and Kosaka, Y. (2018) Indo-western Pacific
climate variability: ENSO forcing and internal dynamics in a
tropical Pacific pacemaker simulation. Journal of Climate,
31(24), 10123–10139. https://doi.org/10.1175/jcli-d-18-0203.1.
Wang, X.L. (2008) Accounting for autocorrelation in detecting
mean shifts in climate data series using the penalized maximal
t or f test. Journal of Applied Meteorology and Climatology,
47(9), 2423–2444. https://doi.org/10.1175/2008jamc1741.1.
Wang, X.L., Wen, Q.H. and Wu, Y. (2007) Penalized maximal t test
for detecting undocumented mean change in climate data
series. Journal of Applied Meteorology and Climatology, 46(6),
916–931. https://doi.org/10.1175/jam2504.1.
Wong, C.-L., Yusop, Z. and Ismail, T. (2018) Trend of daily rainfall
and temperature in Peninsular Malaysia based on gridded data
set. International Journal of Geomate, 14, 65–72. https://doi.
org/10.21660/2018.44.3707.
Wu, H.-T.J. and Lau, W.K.-M. (2016) Detecting climate signals in
precipitation extremes from TRMM (1998–2013)—increasing
contrast between wet and dry extremes during the “global
warming hiatus”. Geophysical Research Letters, 43(3), 1340–
1348. https://doi.org/10.1002/2015GL067371.
Wu, M.C. and Chan, J.C.L. (1995) Surface features of winter monsoon surges over South China. Monthly Weather Review, 123(3),
662–680.
https://doi.org/10.1175/1520-0493(1995)123<0662:
Sfowms>2.0.Co;2.
Xie, S.-P., Hu, K., Hafner, J., Tokinaga, H., Du, Y., Huang, G. and
Sampe, T. (2009) Indian Ocean capacitor effect on Indo–western
Pacific climate during the summer following El Niño. Journal of
Climate, 22(3), 730–747. https://doi.org/10.1175/2008jcli2544.1.
Xie, S.-P., Kosaka, Y., Du, Y., Hu, K., Chowdary, J.S. and Huang, G.
(2016) Indo-western Pacific Ocean capacitor and coherent climate anomalies in post-ENSO summer: a review. Advances in
Atmospheric Sciences, 33(4), 411–432. https://doi.org/10.1007/
s00376-015-5192-6.
24
Yahya, N., Lim, C.-S., Jamaluddin, U. and Pereira, J. (2016) The
December 2014 flood in Kelantan: a post-event perspective.
Newsletter of the Geological Society of Malaysia, 41, 54–57.
Available at: https://ancst.org/wp-content/uploads/2016/05/
The-December-2014-Flood-in-Kelantan.pdf.
Yang, J., Liu, Q., Xie, S.-P., Liu, Z. and Wu, L. (2007) Impact of the
Indian Ocean SST basin mode on the Asian summer monsoon.
Geophysical Research Letters, 34(2), L02708. https://doi.org/10.
1029/2006GL028571.
Zin, W.Z.W., Jamaludin, S., Deni, S.M. and Jemain, A.A. (2010)
Recent changes in extreme rainfall events in Peninsular
Malaysia: 1971–2005. Theoretical and Applied Climatology,
99(3–4), 303–314. https://doi.org/10.1007/s00704-009-0141-x.
NG ET AL.
SU PP O R TI N G I N F O RMA TI O N
Additional supporting information may be found in the
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How to cite this article: Ng, C. Y., Wan Jaafar,
W. Z., Mei, Y., Othman, F., Lai, S. H., & Liew, J.
(2022). Assessing the changes of precipitation
extremes in Peninsular Malaysia. International
Journal of Climatology, 1–24. https://doi.org/10.
1002/joc.7684
Yiwen Mei