Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia

Abstract

Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models against a gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall from 1981 to 2014. Taylor Skill Score was used for ranking the performance of individual models for mean monthly, June–September, and February–May seasonal rainfall. Comprehensive rating metrics (RM) were used to derive the overall ranks of the models. Results show that the performances of the models were not consistent in reproducing rainfall distributions at different statistical metrics and timeframes. More than 20 models simulated the largest dry bias on high topographic and rainfall-receiving areas of the country during the June–Septem-ber season. The RM-based overall ranks of CMIP6 models showed that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. The ensemble of these four Global Climate Models showed the best performance in representing the spatiotemporal patterns of the observed rainfall relative to the ensembles of all models. Generally, this study highlighted the existence of dry bias in climate model projections for Ethiopia, which requires bias adjustment of the models, for impact assessment.