Daniel Berhanu a,b,*, Tena Alamirewa, Meron Teferi Tayec, Degefi Tibebed, Solomon Gebrehiwota
and Gete Zelekea
a Water and Land Resource Center, Addis Ababa University, Addis Ababa, Ethiopia
b Ethiopia Institute of Water Resource, Addis Ababa University, Addis Ababa, Ethiopia
c International Water Management Institute, Addis Ababa, Ethiopia
d Alliance Bioversity-International Center for Tropical Agriculture (CIAT), Addis Ababa, Ethiopia
*Corresponding author. E-mail: danielberhanu217@gmail.com, daniel.b@wlrc-eth.org



Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models
against gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall
from 1981 to 2014. Taylor diagrams and Taylor Skill Score were 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–September 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 GCMs 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.


Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia – Download