Assessment demonstrates model performance in predicting rainfall in Ethiopia, highlighting the need for accurate climate forecasts.
There is a need for accurate climate model simulations to understand climate change and its socioeconomic implications. The main objective of this study was to evaluate the performance of twenty global climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) over Ethiopia. Enhancing National Climate Services (ENACTS) and Climate models rainfall data from 1981 to 2014 were utilized for model performance evaluation in this study. The performances of the models were evaluated with statistical metrics of Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Percent Bias (PBIAS). The findings of this study indicated that most models had similar trends with the ENACTS. Among twenty climate models, six models such as ACCESS-ESM1-5, CanESM5, CNRM-ESM2-1, MPI-ESM1-2-LR, NorCPM1, and SAM0_UNICON underestimated the rainfall. Based on the statistical metrics values of correlation coefficient (CC), MPI-ESM1-2-LR (0.99), BCC-CSM2-MR (0.98), MIROC-ES2L (0.96), NorESM2-MM (0.96), and EC_Earth3_CC (0.96) are best performing models. For all models but IITM-ESM and MRI-ESM2-0, RMSE values were below 5 mm and PBIAS values were within a desirable range (-3.94 to 4.3). These results underscore the importance of selecting appropriate models for evaluating climate impacts, particularly for extreme rainfall events over Ethiopia.
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Mekonnen et al. (2025) studied this question.
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