Networks of rain‐gauges provide an accurate but highly localized measure of rainfall, with limited coverage and resolution, whereas radars provide rain‐rate and accumulation estimates over wide areas at high spatial and temporal resolution but low accuracy. When quantifying rainfall accumulations for applications such as flood forecasting, combining the two sets of data can be beneficial for producing a high‐resolution merged product with a lower error than the gauge‐only or the radar‐only product. In this study, three kriging methods (kriging with external drift (KED), kriging with radar‐based error correction (KRE) and ordinary kriging (OK)) as well as a multiquadric (MQ) scheme have been used to merge radar and gauge data. The results were cross‐validated with true rainfall readings at the surface for a number of different meteorological events covering England and Wales. Overall, all the merging schemes trialled produced a merged product that was superior to the individual radar or gauge data. The KED was the best performing method across all rainfall thresholds and meteorological conditions, with the use of a parametric variogram best suited to short (15 min) accumulation periods and a non‐parametric variogram preferable for hourly accumulations. The study also shows that the merged product deteriorates when the gauge‐network density is reduced; exceptions to this are made in situations where spatially isolated rainfall is observed by the radar and where the gauges that are found least likely to represent the rainfall are carefully removed prior to merging. The latter process was found to improve the quality of the merged product, regardless of the meteorological conditions, providing a viable method for producing a KED‐based merging scheme that is applicable to all meteorological conditions.