Evaluation of precipitation simulations in <scp>CMIP6</scp> models over Uganda

Abstract This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981–2014. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatology, seasonal rainfall distribution and trend. Statistical metrics used include mean bias error, normalized root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models' performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown inter‐annually. Some models could not capture the rainfall patterns around local‐scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL‐ESM4, CanESM5, CESM2‐WACCM, MRI‐ESM2‐0, NorESM2‐LM, UKESM1‐0‐LL, and CNRM‐CM6‐1. The models CNRM‐CM6‐1, and CNRM‐ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. Caution should be taken when employing the models in seasonal climate change studies as their performance varies from one season to another. The model spread in CMIP6 over the study area also calls for further investigation on the attributions and possible implementation of robust approaches of machine learning to minimize the biases.