Summer Heatwaves Trends and Hotspots in the Barcelona Metropolitan Region (1914-2020) Carina Serra (  carina.serra@upc.edu ) Universitat Politècnica de Catalunya, UPC Xavier Lana Universitat Politècnica de Catalunya, UPC Maria-Dolors D. Martínez Universitat Politècnica de Catalunya, UPC Blanca Arellano Universitat Politècnica de Catalunya, UPC Josep Roca Universitat Politècnica de Catalunya, UPC Rolando Biere Universitat Politècnica de Catalunya, UPC Research Article Keywords: heatwaves, maximum temperature, minimum temperature, time trends, Mann-Kendall test, land surface temperature, Modis, hotspots, WeMO, PCA, Barcelona Posted Date: September 30th, 2022 DOI: https://doi.org/10.21203/rs.3.rs-2095725/v1 License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/31 Abstract Daily maximum, TX, and minimum, TN, temperatures recorded at Fabra Observatory, for the period 1914– 2020 (from June to September), have permitted to identify the daytime and nighttime heatwaves, HWs, at Barcelona Metropolitan Region, BMR, along 107 years. Four indices have been computed both for maximum and minimum temperatures heatwaves: the number of events per season, HWN; their maximum duration, HWD; their frequency of occurrence, HWF; and the amplitude of the hottest day event, HWA. Trends in these indices have been obtained and their statistical signi cance quanti ed by means of the Mann-Kendall test. For the whole period (1914–2020), positive signi cant trends have been detected for the four indices corresponding to maximum and minimum temperatures. Nevertheless, when 30-year moving window datasets have been analysed, trends of different signs are obtained. The change in these signs, around 1960s, is outstanding, as well as the behaviour of the heatwaves since year 2000 for TX, and since 1985 for TN, when every year has at least one episode with high duration and amplitude. The convenience of using the four HW indices is evaluated applying a Principal Component Analysis, PCA. Additionally, the spatial distributions of the Modis land surface temperatures, LST, corresponding to some extreme heatwaves, permits the detection of two hotspots in the BMR, one of them for TX and the other for TN. It is also worth mentioning that correlations between Western Mediterranean Oscillation index, WeMOi, and HW episodes are detected, being notable that at the beginning of these episodes WEMOi values are usually lower. 1. Introduction The Mediterranean region, with a dense population, is one of the most affected by the Climatic Change due to the increasing frequency and intensity of heatwaves (Della-Marta et al., 2007; Fisher and Shär, 2010; Perkins et al., 2012, 2020; Thiébault et al., 2016; Winter et al., 2016; Lorenzo et al., 2021; Qasmi et al. 2021), prolonged droughts and higher summer temperatures (Hoegh-Guldberg et al., 2018; Zampieri et al., 2009; IPCC 2021). The increasing frequency of occurrence of extreme events affects the socioeconomic status of these countries and the health and mortality of their population (Amengual et al., 2014; Miralles et al., 2019). Since 1980s the evidence of positive trends in temperatures, accompanied by the increasing of the atmospheric concentration of greenhouse gases, is con rmed around the world and especially in the Mediterranean region (IPCC, 2022; Molina et al., 2020). Since year 2000, various severe heatwaves have affected European countries (Russo et al., 2015; Tomczyk and Bednorz, 2016). An outstanding example is the severe heat wave of the year 2003 (Stott et al., 2004; Fisher, et al., 2007; Rebetez et al., 2009; Trigo et al., 2009; Dousset et al., 2011) with huge economic losses (García-Herrera et al, 2010) and a signi cant mortality of elderly people in France, but also in Germany, Italy, Portugal and Spain. More recently, the heatwave of late June 2019 (Sousa et al. 2019, Xu et al. 2020) affected Central and Western Europe, with several cities reaching temperature records. These European HWs were caused by an anomalous long-lasting anticyclone in the upper troposphere, causing warm air advection from the South. Page 2/31 The Metropolitan Region of Barcelona, BMR, located at the NE of the Iberian Peninsula, has a typical Mediterranean climate (Lionello, 2012; Thiébault et al., 2016) characterised by warm or hot summers, droughts, long dry spells along the year, and extreme rainfall during autumn (Burgueño et al., 2002; Martinez et al., 2010; Serra et al., 2014; Llasat et al., 2016; Lana et al., 2020; López-Bustins et al., 2020). A forthcoming increase in frequency, duration and intensity of heatwaves is particularly concerning in very densely populated cities like Barcelona, where these episodes are enhanced by the urban heat island, UHI, phenomenon (Moreno-García, M.C., 1994, Salvati et al., 2017), these cities becoming more exposed to the effects of global warming. The heatwaves occurred in Spain since 1975 have been analysed by AEMET (2018). More recently, Sanchez-Benítez et al. (2020) have investigated the linkage between Iberian HWs and atmospheric circulation patterns, and Lorenzo et al. (2021) have derived future heatwave intensity in the Iberian Peninsula, using temperature projections with a spatial resolution of 12 km, under two future warming scenarios. The present research is focused on a smaller spatial scale, with the aim of detecting the main hotspots in the BMR when a heatwave episode occurs. For this purpose, the land surface temperature, LST (Modis Satellite data of 1 km2 resolution), is used. First, the completeness and high quality of the maximum and minimum temperature records of the Fabra Observatory (Serra et al., 2001; Burgueño et al., 2002; Lana et al. 2009; Prohom et al., 2016), for years 1914–2020, permits the identi cation of the daytime and nighttime heatwaves for a long period and the study of the temporal evolution and trends of different HW indices. Second, a PCA has been applied with the aim of reducing the number of indices describing a HW, facilitating in this way the interpretation of the results. Third, the location of the hotspots in the BMR has been detected and represented. Finally, this research is complemented with an analysis of the WeMO indices corresponding to HW days, searching for relationships between WeMO and HW data. 2. Database And Methodology 2.1 Database and study area The maximum, TX, and minimum, TN, temperature series used for the analysis of the heat waves in Barcelona has been obtained from the Fabra Observatory (Royal Academy of Art and Sciences of Barcelona) (41.418° N, 2.124° E), placed in the Barcelona Metropolitan Region, at 6,5 km distance from the Mediterranean shoreline (Figs. 1a and 1b). This location, at a moderate altitude of 411 m.a.s.l. in the Littoral Chain, surrounded by pine trees and away from buildings, is much less affected by the UHI effects than other thermometric stations within Barcelona city. This thermometric series is one of the longest and complete of Spain, without any gap all along the 107-year period considered (1914–2020) and the World Meteorological Organization (WMO) awarded Fabra Observatory with the title of centenarian station in 2018. The quality of the series, considered homogeneous at a 95% level, has been previously assessed by Quereda Sala et al. (2000), Serra et al. (2001); Lana et al. (2009) and Prohom et al., (2016), among others. Page 3/31 The metropolitan region of Barcelona, BMR, with an extension of 3242.2 km2 and a population density of 1566.2 inhabitants/km2, is one of the most crowded areas in the Mediterranean region. Particularly, Barcelona city, with a population of 1.6 million inhabitants, covers an area close to 100 km2. It is delimited by the Littoral Chain, the Mediterranean shoreline and the Llobregat and Besós rivers (Fig. 1a). Daily maximum and minimum temperatures of 48 stations (Fig. 1c), covering the BMR area, for the year 2015, have been also used in order to complete the study of the spatial distribution of hotspots related to summer HWs. These stations, provided by the Servei Meteorològic de Catalunya (SMC) and the Agencia Estatal de Meteorología (AEMET, Spain), were previously used in Serra et al. (2020). As a preliminary analysis of the Fabra Observatory HWs, Fig. 2a shows, for every year, the maximum TX and TN. From 1914 to 1970, non-signi cant negative trends are observed for TX and TN. However, for the period 1970–2020, outstanding signi cant positive trends of 0.5 and 0.6oC/decade are obtained. 2.2 Methodology Heatwave de nition A heatwave, HW, is detected when a number of consecutive days reach temperatures over a certain hot threshold. Nevertheless, several hot threshold criteria exist to compute the consecutive days of a HW (Della-Marta 2007, Fisher and Schär, 2010; Perkins and Alexander, 2013; Russo et al., 2015; Vogel et al., 2020; Kostyrko et al., 2022). In this paper, we assume that a HW is detected when three or more consecutive days reach temperatures over the 90th percentile of each calendar day for summer months (Perkins et al. 2012). These percentiles are obtained with a 15-day moving window of daily TX and TN over 1914–2020, from June to September. Along this period, 148 heatwaves for TX and 163 for TN have been detected, large enough numbers to assure the statistical signi cance of the analyses. Figure 2b shows the 90th, 95th and 98th percentiles for Fabra Observatory, varying the corresponding thresholds with the summer calendar day. The maximum values are reached around the rst days of August with 31,5oC and 22oC for the 90th percentiles of maximum temperature, TX90, and minimum temperature, TN90, respectively. The minimum thresholds correspond to the end of September (25oC) for TX90, and the rst days of June (17 oC) for TN90. Heatwave indices and trends Over the last years, several heatwave indices have been de ned, depending on the region and the proposed research. In this paper, according to Fisher and Schär (2010), four indices are computed for daytime and nighttime (TX and TN) heatwaves: the number of events per season, HWN, the amplitude of the hottest day event per season, HWA, the length of the longest event (days), HWD, and the total number of days satisfying heatwave criterion, HWF. Time trends for each heatwave index are obtained by means of the least square method and their statistical signi cance assessed by the Mann–Kendall statistic (Sneyers, 1990) at the 95% level of signi cance. They are obtained for the whole recording period and for 30-year moving window subsets. Page 4/31 Extreme heatwave and hotspots In this research, an extreme heatwave is de ned as an episode with three or more consecutive days with temperatures higher than the 98th percentile. The main characteristics of these episodes, as well as the spatial distribution of temperatures across the BMR, are analysed. For this purpose, the daily MOD11A1 LST measured by MODIS Terra, including daytime 10:30 UTC surface temperature, LSTd, and night time 22:30 UTC, LSTn, with 1 km2 spatial resolution, has been used. Even though these temperatures are not equal to the air temperatures at 2 m height above ground, are highly correlated with air maximum, TX, and minimum, TN, temperatures. These correlations and the multiregression equations were obtained for the BMR in Serra et al. (2020). To con rm the hotspots detected with LST values, the spatial distributions of the daily air temperatures, TX and TN, of 48 thermometric stations are represented. First, the representation is applied to some particular days pertaining to an extreme HW. Second, a principal component analysis (Jolliffe, 1986) is applied to the summer of 2015 and the spatial patterns of the obtained rotated principal components, RPCs, are illustrated. PCA and HW indices The diversity of HW indices used in different researches and the uncertainties concerning the independence among these indices, lead us to consider the analysis of the correlation within them. With this aim, the Pearson correlation coe cient is computed so that possible relationships between previously assumed independent variables can be detected. Additionally, the rotated principal component analysis (RPCA) (Jolliffe, 1986; Richman, 1986; Preisendorfer, 1988) is applied. According to the Kaiser criterion, only PCs with eigenvalues greater than or equal to 1.0 are extracted. The retained PCs are then rotated by means of the varimax orthogonal technique, in order to reduce data dimensionality. In this way, more detailed characteristics of relationships between independent variables can be established; particularly, the ratio of data variance explained by every rotated principal component, RPC, and the contribution (factor loading) of every independent variable in the RPCs. WeMO and HW The Western Mediterranean Oscillation index, WeMOi was de ned by Martin-Vide and Lopez-Bustins (2006) as the difference in surface atmospheric pressure between Padua, Italy (45°24′ N, 11°47′ E) and San Fernando, Spain (36°17′ N, 06°07′ W). Although the high correlation between torrential episodes in the Spanish Mediterranean coast and the WeMO has been extensively proved (Martin-Vide and LopezBustins, 2006; López-Bustins et al., 2020), the correlation with temperatures is not so evident. El Kenawy et al. (2013) detected a signi cant linkage for the period 1960–2006 between the negative phase of WeMO and maximum temperatures during the warm season in NE Spain. However, opposite results were obtained for the Iberian Peninsula for the period 1994–2013 (Mohammed et al., 2018), concluding that the values of the extreme hot temperatures are higher for years with a positive WeMO phase. Due to the mentioned controversies about the relationship between the WeMOi and hot summer temperatures, this research would contribute to a new analysis of the possible correlations, restricted to Barcelona city, Page 5/31 although they could also be assumed for the Barcelona metropolitan area, very close to the north-western coast of the Mediterranean. 3. Results And Discussion 3.1 HW indices and trends The results concerning HW indices and their time trends are shown in Figs. 3 to 8. Figures 3 and 4 show the time evolution of the four HW indices, for TX and TN. Trends and their statistical signi cances for TX, computed for 30-year moving window (30-YMW) subsets, are represented in Figs. 5 and 6 respectively, and those corresponding to TN are shown in Figs. 7 and 8. Number of events per season, HWN The time evolution of HWN_TX, (Fig. 3a), shows that, up to 1980, only in 1928 there is an outstanding value of four events, while since 1980 there are 13 years with a number between 4 and 7 events per season, 10 of these years corresponding to the period 2000–2020. In fact, since 2000, every year has suffered at least one heatwave episode. The HWN_TN index (Fig. 4a) depicts an outstanding value of 7 events in year 2003, and since 1985, all years have at least one heatwave episode. The trends for different periods are summarised in Table 1. All trends are positive and signi cant at the 5% level. For the whole recording period (1914–2020), the HWN_TX trend is positive and equal to + 0.29 events/decade. Nevertheless, the maximum trend is reached for the last period 1990–2020, with + 1.38 heatwaves per decade. In agreement with this trend, the HWN increment would be of 11 episodes per season in 2100. If the reference is the trend of the whole period 1914–2020, the increment would be lower, 2.5 events per season. For TN, the trends obtained for the different periods are signi cant, as for the maximum temperature, though with lower values. The maximum trend is obtained again for the period 1990–2020, with a value of + 0.64 events/decade, being likely an increment of 5 HWs per season in 2100. Trends computed for 30-YMW subsets (Figs. 5a and 6a), since year 2000, are systematically greater than + 0.5 events/decade and statistically signi cant. Another period with signi cant positive trends is around 1970–1980. Instead, signi cant negative trends are obtained for 1940–1960. For HWN_TN, maximum positive signi cant trends are reached in 1990s (Figs. 7a and 8a). Page 6/31 Table 1 Time trends obtained for the four HW indices, for TX and TN and different time periods. HWN HWA HWD (days/dec) (ev./dec) (oC/dec) HWF (days/dec) 1914–2020 TX90 + 0.29 + 0.28 + 0.50 + 1.48 TN90 + 0.21 + 0.19 + 0.35 + 1.08 1950–2020 TX90 + 0.62 + 0.71 + 1.14 + 3.05 TN90 + 0.39 + 0.47 + 0.89 + 2.22 1960–2020 TX90 + 0.73 + 0.77 + 1.28 + 3.59 TN90 + 0.45 + 0.50 + 0.96 + 2.64 1970–2020 TX90 + 0.82 + 0.77 + 1.37 + 4.00 TN90 + 0.63 + 0.77 + 1.34 + 3.52 1980–2020 TX90 + 0.78 + 0.43 + 1.13 + 3.81 TN90 + 0.61 + 0.70 + 1.30 + 3.70 1990–2020 TX90 + 1.38 + 1.02 + 1.52 + 6.17 TN90 + 0.64 + 0.69 + 1.18 + 4.01 Amplitude of the hottest event per season, HWA The maximum amplitudes for TX (Fig. 3b), are close to 7oC, mainly concentrated in the last years. The maximum of 8.7oC should be carefully considered, as it would be a consequence of a forest re occurred very close to Barcelona city in 1982. For TN (Fig. 4b), there are several amplitudes around 6oC spread out along the whole period and a notable maximum of 7.5oC (year 1931) and a second maximum of 6.6oC (year 1923). Signi cant positive trends from + 0.28 to + 1.02oC/decade (for TX) and from + 0.19 to + Page 7/31 0.77oC/decade (for TN) are obtained for the different analysed periods (Table 1). While for HWA_TX the maximum trend corresponds to the last period (1990–2020), as observed for HWN index, the maximum trend for HWA_TN is detected in the period 1970–2020. Amplitude increments varying from 1.6 to 8.2oC, depending on the period trend considered, could be expected in 2100. Consequently, the average increment would be close to 5oC with respect to the present values. As an example, taken as a reference the maximum temperature of 38.4oC reached in 2003 (Table 2), it could be likely to reach maximum temperatures around 40oC for the near future (2050) and of 44oC at the end of the 21st century in Barcelona. For the 30-YMW subsets (Figs. 5b and 6b), the maximum HWA_TX trend is reached in the 1970s with signi cant positive trends around + 1.5oC/decade. Since 2000s up to nowadays, the trends are signi cantly positive again. Instead, the maximum trends for HWA_TN are reached 20 years later, in 1990s, around + 1.0oC/decade (Figs. 7b and 8b). Length of the longest event per season, HWD The maximum values reached by HWD index are 15 and 14 days in 2003 for TX and TN, respectively (Figs. 3c and 4c). Others years reached values greater or equal 10 days. For TX, values of 10–12 days are reached in years 1947, 1982, 1987, 2005 and 2009. For TN, values of 11–12 days occur in 1923, 1947 and 2006. Table 1 shows signi cant positive trends for all the periods considered, with values from + 0.50 to + 1.52 days/decade for HWD_TX and from + 0.35 to + 1.34 days/decade for HWD_TN. It would imply that increases from 3 to 12 days in the maximum HW duration could be expected in 2100. By comparing these trends with those obtained by Perkins-Kirkpatrick and Lewis (2020) for the Mediterranean region (+ 0.61 days/decade) in the period 1950–2020, it is observed that the trends derived for Barcelona are slightly greater (+ 0.89 days/decade for TN and + 1.14 days/decade for TX). When the 30-YMW trends are analysed (Figs. 5c, 6c, 7c and 8c), clear decreasing trends until 1960 and an abrupt change to higher positive trends since 1960 until the end of the period, are observed. This behaviour is detected in both temperatures TX and TN, but with a higher sharp increase for TX. For HWD_TX, maximum trends are of 1.5 days/decade for the 1970–1980 years and since year 2000. For HWD_TN maximum trends are slightly higher, 2.0 days/decade, during 1990s. Number of heatwave days per season, HWF The year 2003 is the most outstanding, with 47 and 55 days heatwave days for TX and TN, respectively (Figs. 3d and 4d). Table 1 shows that trends for the different studied periods range from + 1.5 to + 6.2 days/decade for TX and from + 1.1 to + 4.0 days/decade for TN, again with greater values for TX. Consequently, an extreme increment of 48 days per season for HWF_TX and of 32 days for HWF_TN in 2100 would be possible. The HWF-trend obtained for the Mediterranean region by Perkins-Kirkpatrick and Lewis (2020) was + 2.61 days/decade for the period 1950–2017, in agreement with the values summarised in Table 1 (+ 3.05 days/decade for TX and + 2.22 days/decade for TN) for an almost coincident period. (1950–2020). Whit respect to the 30-YMW trends (Figs. 5d and 7d), a maximum of 6 days/decade is obtained for both TX and TN in the mid 2000s and 1990s, respectively. The Mann-Kendall Page 8/31 test coe cients (Figs. 6d and 8d) are signi cant for the periods 1965–1982 and 1995–2005 for TX, and for 1985–1997 and 2002–2005 for TN. These different time periods detected for TX and TN manifest the discrepancies between the time behaviour of maximum and minimum temperatures. Page 9/31 Table 2 Date, duration and maximum temperature of the HWTX and HWTN extreme events (exceeding 98th percentiles of calendar day TX and/or TN). HWTX98 HWTN98 Date D ( rst day) (days) TXmax Date D ( rst day) (days) 02/09/1930 3 22.8 10/06/1931 4 26.6 3 30/08/1944 4 24.0 4 23/07/1947 6 26.8 13/08/1987 3 27.2 16/09/1987 4 23.6 30/07/2001 3 24.8 (oC) 1 2 11/06/1931 3 35.3 5 12/06/1981 5 35.1 6 05/07/1982 5 39.8 7 8 16/09/1987 5 32.4 9 TNmax (oC) 10 11/06/2003 5 34.9 11/06/2003 5 25.2 11 18/06/2003 5 35.4 19/06/2003 3 24.0 10/07/2003 3 24.4 03/08/2003 11 27.8 14 18/06/2005 3 23.7 15 10/07/2006 3 24.8 12 13 02/08/2003 13 38.4 16 30/06/2009 3 34.8 17 16/08/2009 4 37.7 18 12/09/2011 4 30.4 19 18/08/2012 5 35.2 19/08/2012 4 26.3 20 03/07/2015 3 35.0 04/07/2015 3 26.0 21 03/09/2016 3 32.4 03/09/2016 3 23.4 10/06/2017 6 23.7 22 23 02/08/2018 3 37.2 01/08/2018 4 26.8 24 26/06/2019 3 37.7 26/06/2019 4 27.5 Page 10/31 HWTX98 HWTN98 25 14/09/2020 3 22.7 3.2 Extreme HWs and hotspots Table 2 shows date, duration and maximum, TX or TN, for the extreme heatwave episodes occurred all along the period considered (1914–2020). During these extreme events, in three or more consecutive days, TX and/or TN exceeded the corresponding calendar-day 98th percentiles. Most of these extreme episodes occurred after the year 1980 (21 out of 25) and especially since 2000 (17 out of 25). It is outstanding the extremely severe episode of august 2003, with maximum TX of 38.4oC and maximum TN of 27.8oC, and durations of 13 and 11 days respectively. A second outstanding episode occurred in June 2019 with extreme TX and TN of 37.7 oC and 27.5 oC, respectively. This second episode was shorter than the rst (3 and 4 days), but also very remarkable since it occurred in June, when is rather unusual to suffer extreme heatwaves with so higher extreme TX and TN. It is also worth noting that 10 out of the 25 HW events along the period considered are compound TX and TN (daytime-nighttime) heatwaves. A majority of these compound episodes (8 out of 10) have occurred since 2000, three of them in 2003 and the remaining ve since 2012. An eventual increase in the frequency of occurrence of these daytime-nighttime HW events could be a matter of concern, given that the exposure of population to daytime hot conditions with little or no relief from nighttime cooling may result in increased heat-stress related hazards to human health (Meehl and Tebaldi, 2004; Li et al., 2017; Wang et al., 2020). Figures 9 and 10 depict the spatial distribution of the land surface temperature, LST, obtained from the Modis satellite for day (LSTd) and night (LSTn), for six extreme HW episodes. The differences between the spatial distribution of LSTd and LSTn are evident. While for diurnal maps the greater LST values are reached in the inner valleys and basins (Llobregat, Vallès and Penedès), for LSTn the higher values are attained mainly in Barcelona city, notably suggesting the evidences of the UHI effect. The exception is the episode of June 28th 2019, when the LSTn highest values spread all over both coastal and inner locations. The LSTd of this outstanding episode, represented in Fig. 9, depicts a pattern similar to the rest of the LSTd maps, but showing the highest maximum LST values, greater than 50oC, in some locations of the Vallès valley. Even though the LST values are not equal than the air temperatures at 2-metres height, these maps are a rst insight into the location of the hotspots of the BMR. Figure 11a shows the spatial distribution of TX and TN recorded on July 5 and July 29, 2015, using the temperature data of 48 thermometric stations in the BMR (Serra et al., 2020). For July 5th, pertaining to the extreme HW of 2th -7th July, the two hotspots just observed in the LST maps, are again noticed, with Page 11/31 a smoothed resolution, the highest TX observed in inner locations and the highest TN in Barcelona city. Instead, for an example out of the HW episode, July 29th, the maximum values are more spread out for both TX and TN. A Principal Component Analysis, PCA, is applied to TN recorded in July and August 2015. Figure 11b depicts the spatial distribution of the factor scores corresponding to the two rotated principal components, RPCs, obtained. The maximum values of factor scores for July are located at Barcelona for both RPCs, but the UHI effect is clearly manifested in RCP2, with an explained variance of 28%. The RPC2 is highly correlated with the days pertaining to the HW of the rst days of July 2015. For August, the maximum factor scores are detected along the littoral fringe for RPC1 with an explained variance of 54.1%. The RPC2 factor scores for August show higher values (40.5% of variance) in the inner regions of the BMR. 3.3 PCA of HW indices Table 3 shows the correlations among the four indices HWN, HWD, HWA and HWF for TX and TN. High correlations are obtained among the TX indices (0.762–0.926), with the highest values corresponding to the correlation between the number of episodes per season, HWN, and the total number of heatwave days per season, HWF. The correlations among the TN indices are slightly lower (0.648–0.927). When the correlations among TX and TN indices are computed, the coe cients are lower, with values between 0.49 and 0.77. The possible redundancies in the information provided by the four indices are con rmed by the values of the factor loadings for the RPCs shown in Table 4. Applying the eigenvalue equal to 1.0 criterion, two principal components are retained, thus explaining a total variance of 85.7%. While the rst rotated principal component, RPC1, is highly correlated with the TN indices, the second, RPC2, is notably related to the TX indices, and each one of the RPCs explain almost the same variance, 43.3% and 42.4%, respectively. Although the HW indices considered in this research are those proposed by Fisher and Schär (2010), after applying the RPCA, these eight indices can be replaced by two rotated principal components, one for TX and the other for TN. The time evolution of the RPC1 and RPC2 factor loadings is illustrated in Fig. 12. It is remarkable the increasing values since 1980s, with an outstanding maximum in 2003 for RPC1 and in 1982 for RPC2. Two secondary maxima are reached in 2009 and 2013 for RPC2. 3.4 HW and WeMO With the aim of investigating the possible relationship between the WeMO index and the occurrence of HW episodes, Fig. 13a shows the time evolution of the average summer WeMO index from 1914 to 2020. It is outstanding the negative trend during all the period. The evolution of the annual WeMO analysed by the Climatology Research Group of the University of Barcelona (http://www.ub.edu/gc/wemo/) for the period 1821–2020 re ects a clear positive trend from 1821 to 1910 approximately, and a negative trend from 1910 until 2020. Then, the period studied in this paper is in the phase of decreasing values of WeMOi. A rst step has consisted on the study of the correlation between WeMOi and daily TX and TN, which leads to non-conclusive results, given that small correlation coe cients have been obtained. Nevertheless, some non-negligible negative correlations have been obtained between annual summer Page 12/31 WeMOi and HW indices (Table 5). Figure 13b illustrates the summer WeMOi, SWeMOi, histogram, where the highest frequency corresponds to values between − 0.5 and + 0.5. However, Fig. 13a shows that, from 1980, the indices are predominantly negative in concordance with the increasing frequency and intensity of HWs from 1980s. The histograms of WeMOi values corresponding to the rst day of the studied HWs are represented in Figs. 13c and 13e. More than 80% of the values are negative and slightly lower for TX than for TN. When the last-day WeMOi of a HW episode is represented (Figs. 13d and 13f), the histograms show higher values. This fact suggests that some signs of a forthcoming HW would start with lower WeMOi values and would nish with higher WeMOi values. Figure 14 depicts two examples of the WeMOi evolution for summer 2003 and 2015. In both cases the HW episodes are coincident with a beginning of WeMOi low values, then increasing along the HW. The synoptic situation associated with the summer HWs (Figs. 15 and 16) usually shows a low pressure in the southwest of Spain and high pressures in central Europe, implying an advection from the south along the Spanish Mediterranean coast. This low-pressure system shifts towards the Iberian Peninsula centre and remains some days there. At 500 hPa, a ridge comes from Africa and extends towards Western Europe. Conclusions Several questions concerning heatwaves in Barcelona have been analysed for a long recording period (1914–2020). On the one hand, time trends and their statistical signi cance, for different sub-periods and for 30-year moving window subsets, have been studied in detail. On the other hand, spatial distributions of hotspots in the BMR, correlations among the different HW indices, and relationships between the WeMOi and the HW in summer, have been analysed. In agreement with climate change studies about global warming, with increasing trends especially since 1980s, the time evolution of the four HW indices in Barcelona also re ects increasing values along the last decades. All the trends for the HW indices, both for TX and TN, corresponding to the whole period (1914–2020), are signi cant and positive. When the 30-year moving windows are applied, it is outstanding the abrupt change in the sign of trends since 1960s for TX and TN, showing signi cant positive trends since 1970s. These trends vary from + 0.5 to + 1.4 events/decade for HWN, + 0.5 to + 1.4oC/decade for HWA, + 1.0 to + 2.0 days/decade for HWD, and + 2.5 to + 6.2 days/decade for HWF. Taking as a basis the intermediate trends corresponding to the period 1970–2020, the projections of the summer season characteristics for the end of the 21th century, would imply an increment of 5 HW episodes by season, a high increment of 6oC in the maximum HW amplitude and a remarkable maximum increase of 10 days in the length of HW events. The spatial distribution of the hotspots in BMR, when extreme HWs are active, shows two different nuclei. While the highest maximum temperatures are recorded in the inland valleys and basins, the hotspots for the minimum temperatures are detected in Barcelona city, directly associated with the UHI phenomenon. It is worth noting that the high minimum temperatures reached in the city during an extreme HW could be quali ed as tropical, or even torrid, with harmful consequences on thermal discomfort of the population during nighttime hours and negative effects on human health. Page 13/31 The reduction of the number of HW indices, by means of Principal Component Analysis (PCA), permits to explain a 85.7% of the total data variance with two RPC. The rst one is notably related to the TN indices and the second one, principally associated with TX indices. The highest correlation, greater than 90%, is obtained between the number of HW events per season, HWN, and the number of HW days per season, HWF. Although possible correlations between WeMOi and HW episodes are almost null between WeMOi and daily TX and TN data, signi cant negative correlations are detected between annual SWeMOi and HW indices. Additionally, the beginning of HWs is predominantly related to low values of WeMOi, then increasing along the evolution of the HWs. As a summary of the obtained results, the detected evolution of HW characteristics towards the end of the 21th century, with remarkable projected increases in the number of HW days and HW events, and in their amplitude and duration, would constitute a matter of concern for Barcelona city and its metropolitan region. Declarations Acknowledgements Rainfall data have been supplied by Servei Meteorològic de Catalunya (Generalitat de Catalunya), Fabra Observatory (RACA, Barcelona) , Agencia Estatal de Meteorología (AEMET, Spanish Government) and Modis satellite data (NASA). This research was supported by the Spanish Ministry of Science, Innovation and Universities: grant number PID2019-105976RB-I00]. The authors also thank the editor and the anonymous reviewers for their valuable comments and suggestions to improve the manuscript. Funding This research has been nanced by the project PID2019-105976RB-I00 (Agencia Estatal de Investigación, Spanish Government). Con ict of interest On behalf of all authors, the corresponding author declares no competing interests. Ethics approval Not applicable. Consent to participate All authors consent to participate into the study. Consent for publication All authors consent to publish the study in a journal article. Data availability Rainfall data used to support the fndings of this study were supplied by Servei Meteorològic de Catalunya (Generalitat de Catalunya), available by request to dades.meteocat@gencat.cat and Agencia Estatal de Meteorologia (AEMET) https://www.aemet.es . Data of the Fabra Observatory (RACA) are free available in Home European Climate Assesment and Dataset (https://www.ecad.eu). Modis Satellite data are free available in https://modis.gsfc.nasa.gov/data/ Code availability Not applicable. Page 14/31 Author contribution CS, XL, DM, BA, JR and RB conceptualization and design. CS, BA, JR and RB data collection. CS methodology and software. CS , XL, DM validation. CS, DM and RB gures. CS rst writing. CS, XL, DM, BA, JR and RB discussion of the results. CS, XL and DM revision of the text. All authors read and approved the nal manuscript. References 1. AEMET, 2018. Olas de Calor en Espana desde 1975. Agencia Estatal de Meteorología. htt p://www.aemet.es/documentos/es/conocermas/recursos_en_linea/publicaciones_y _estudios/estudios/Olas_calor/Olas_Calor_ActualizacionOctubre2018.pd 2. Amengual A., Homar V., Romero R., Brooks H. E., Ramis C., Gordaliza M., Alonso S. (2014) Projections of heat waves with high impact on human health in Europe. 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Zampieri M., D’Andrea F., Vautard R., Ciais P., de Noblet-Ducoudré N., Yiou P.(2009) Hot European Summers and the Role of Soil Moisture in the Propagation of Mediterranean Drought. J. of Climate, 27, 4747-4758. https://doi.org/10.1175/2009JCLI2568.1 Figures Page 18/31 Figure 1 a: Main orographhic features of BMR (source: Google maps). b: Fabra Observatory and a view over Barcelona city (source: RACA). c: Altitude and thermometric stations of BMR, including Fabra Observatory (FBR). Figure 2 a: Yearly summer maximum TX and TN. Trends for the periods 1914-1970 and 1970-2020 b: Daily 90th, 95th and 98th percentiles of maximum and minimum temperature of the Fabra Observatory for summer days (JJAS). Page 19/31 Figure 3 Time evolution (black line) and linear trend (red line) of the number of events (HWN), amplitud of the hottest event (HWA), duration of the longest event (HWD) and frequency (HWF) for maximum temperatures at Fabra Observatory. Page 20/31 Figure 4 Time evolution (black line) and linear trend (red line) of the number of events (HWN), amplitud of the hottest event (HWA), duration of the longest event (HWD) and frequency (HWF) for minimum temperatures at Fabra Observatory. Page 21/31 Figure 5 30-year moving window trends obtained for the four HW indices, for maximum temperatures. Page 22/31 Figure 6 Mann-Kendall statistic for trends obtained from 30-year moving window for the four HW indices, for maximum temperatures. Page 23/31 Figure 7 30-year moving window trends obtained for the four HW indices, for minimum temperatures. Page 24/31 Figure 8 Mann-Kendall statistic for trends obtained from 30-year moving window for the four HW indices, for minimum temperatures. Page 25/31 Figure 9 Land surface temperature spatial distribution for some selected days during daytime HW98 episodes. Page 26/31 Figure 10 Land surface temperature spatial distribution for the same days of Figure 8, during nighttime HW98 episodes. Page 27/31 Figure 11 a: Spatial distribution of the maximum and minimum temperatures recorded on July 5 and July 29, 2015. b: Spatial distribution of RPC1 and RPC2 factor scores corresponding to TN during July and August, 2015. Figure 12 Time evolution of the factor scores corresponding to RPC1 and RPC2. Page 28/31 Figure 13 Annual summer WeMOi trend. Histograms of WeMOi HW episodes. Page 29/31 Figure 14 Daily summer WeMOi and 7-day running average for years 2003 and 2015. Dashed lines show the limits of the HWs. Figure 15 Surface pressure and 500 hPa geoptotential height for August 2003 and June 2019 events. Page 30/31 Figure 16 Surface pressure and fronts for August 2003 and June 2019 events. Page 31/31