Evaluation and selection of subseasonal-to-seasonal (S2S) models for rainfall prediction over Ethiopia

Numerous subseasonal-to-seasonal (S2S) models exist, yet their performance over tropical regions remains poorly understood, leaving forecasters with little guidance on which models best suit operational downscaling. This study evaluates 14 global models from the Copernicus Climate Change Services (C3S) and North American Multi-Model Ensemble (NMME) initiatives for precipitation prediction over Ethiopia. We utilize Taylor diagrams and spatial bias assessments to categorize model performance across monthly, seasonal, and annual scales, using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Enhancing National Climate Services (ENACTS) datasets as references. Our quantitative results reveal substantial inter-model variability, with correlation coefficients ranging from 0.64 to 0.89 and Root Mean Square Error (RMSE) values between 42.97 and 78.38 mm/month. The Deutscher Wetterdienst (DWD), Japan Meteorological Agency (JMA), and European Center for Medium-Range Weather Forecasts (ECMWF) models consistently emerge as top performers, maintaining high correlations (0.85–0.89) and the lowest RMSE values (42.97–47.02 mm/month). Seasonal analysis demonstrates that these three models effectively capture the spatial climatology of the main rainy season (June–September, JJAS) and the October–January (ONDJ) season. However, the JJAS season exhibits the highest one-standard-deviation spread, indicating increased forecast uncertainty during peak rainfall periods. Conversely, the NMME ensemble tends to overestimate precipitation during the February–May (FMAM) season. We find that while individual C3S models generally exhibit higher skill, the full NMME ensemble outperforms the C3S ensemble through beneficial error cancellation. Annual skill scores confirm that a parsimonious ensemble of DWD, JMA, and ECMWF reduces spatial bias more effectively than larger aggregates. Unlike large multi-model ensembles where skill often emerges from the cancellation of opposing errors, this refined subset provides high predictive capability driven by the inherent accuracy of its constituent models. Consequently, we recommend this refined subset for operational services and downscaling at the Ethiopian Meteorological Institute (EMI). This selection optimizes predictive capability for Ethiopia’s tropical climate while reducing computational overhead and simplifying data management.