Performance evaluation of multiple regional climate models to simulate rainfall in the Central Rift Valley Lakes Basin of Ethiopia and their selection criteria for the best climate model.

The historical datasets of five regional climate models (RCMs) available in the Coordinated Regional Downscaling Experiment (CORDEX)-Africa database are evaluated against ground-based observed rainfall in the Central Rift Valley Lakes Basin of Ethiopia. The evaluation is aimed at determining how well the RCMs reproduce monthly, seasonal, and annual cycles of rainfall and quantify the uncertainty between the RCMs in downscaling the same global climate model outputs. Root mean square, bias, and correlation coefficient are used to evaluate the ability of the RCM output. The multicriteria decision method of compromise programming was used to choose the best climate models for the climate condition of the Central Rift Valley Lakes subbasin. The Rossby Center Regional Atmospheric Model (RCA4) has downscaled ten global climate models (GCMs) and reproduces the monthly rainfall with a complex spatial distribution of bias and root mean square errors. The monthly bias varies in the range of - 35.8 to 189%. The summer (wet), spring, winter (dry), and annual rainfall varied within the range of 1.44 to 23.66%, - 7.08 to 20.04%, - 7.35 to 57%, and - 3.11 to 16.5%, respectively. To find the source of uncertainty, the same GCMs but downscaled by different RCMs were analyzed. The test results showed that each RCM differently downscaled the same GCM, and there was no single RCM model that consistently simulated the climate conditions over the stations in the study regions. However, the evaluation finds reasonable model skill in representing the temporal cycles of rainfall and suggests the use of RCMs where climate data is scarce after bias correction.