Evaluation of CMIP6 model performance and extreme precipitation prediction in the Awash basin

Extreme rainfall and its accompanying hydrological extremes are happening more frequently as a result of global warming's alteration of regional and local weather patterns. This poses a serious risk to ecosystem, environment and the community livelihoods. The Awash basin in Ethiopia is especially vulnerable to these events, posing significant threats to the region. There are, however, limited information's available that could be used to characterize the condition of extreme precipitation in the basin. Therefore, this study aims to evaluate the performance of CMIP6 models in simulating extreme precipitation in the Awash basin. Additionally, the study calculated extreme precipitation using best-fit probability distribution functions (PDFs) for the period from 1985 to 2014. The Climate Hazards Group Infrared Precipitation with station data (CHIRPS) were used to evaluate the global climate models. Simulated data were interpolated using bilinear techniques. Four statistical indices (percentage of bias, root mean square error, mean absolute error, and Pearson correlation) assessed GCM performance in simulating precipitation extremes. Graphical approaches, numerical methods, and empirical distribution functions were employed to evaluate the performance of various probability distribution functions (PDFs). The study identified MIROC6, CESM2-WACCM, and Ensemble as well-performing models with PBIAS and RMSE of 6.6 %, −10.2 %, −17.2 %, and 11.5, 10, 9.7 respectively, while MPI-ESM1-2-HR and EC-Earth3 struggled with extreme rainfall simulation. The generalized extreme values distribution was found to be a good fit for extreme rainfall estimation. GFDL-ESM4 and BCC-CSM2-MR models estimated the highest extreme rainfall of 90 mm/day and 80 mm/day, respectively, however these models overestimated the return period. Conversely, MRI-ESM2-0, NorESM2-MM, ACCESS ESM1-5, and CMCC-ESM2 models underestimated the return periods. Spatially, GFDL-ESM4 and ACCESS-ESM1-5 models exhibited uniform peak rainfall values over a large area. Overall, the study suggests that employing the generalized extreme value distribution could effectively inform risk assessment and management of extreme events in the Awash basin.