Regional climate models (RCM) are commonly used to downscale the coarse resolution general circulation models (GCMs) to produce climate variables at spatially high-resolution grids. The quality of the downscaled data depends on the skills of both GCMs and RCMs. In this study, 10 GCMs are used to constrain the boundary and provide initial conditions of three RCMs. A total of 18 GCM-RCMs combinations are employed to produce simulations over East Africa (EA). The accuracy of simulated rainfalls is evaluated with respect to Climate Research Unit (CRU) rainfall to identify the best GCM-RCM combinations. Bias, root mean squared error (RMSE), correlation coefficient, and MAE-based model skill score have shown that MPI-REMO, MIROC-REMO, MPI-RCA4, IPSL-RCA4, CCCMA-RCA4, MOHC-CCLM, MOHC-REMO, and CNRM-RCA4 during spring season; ICHEC-REMO, MIROC-REMO, MOHC-REMO, MIROC-RCA4, CSIRO-RCA4, and MPI-REMO during autumn season; CSIRO-RCA4, MIROC-RCA4, CCCMA-RCA4, MIROC-REMO, CNRM-RCA4, and MOHC-RECA during boreal summer; and ICHEC-REMO, NOAA-RCA4, MOHC-REMO, MOHC-CCLM, MIROC-REMO, MPI-REMO, and IPSL-RCA4 during boreal winter season are the best performing GCM-RCM combination. It is also evident that the skills of the models are better in autumn than their skills in boreal spring and summer. Moreover, summer rain in EA is the most difficult for models to simulate. Comparison of annual mean with the CRU rainfall shows that MPI-REMO, MIROC-REMO, CSIRO-RCA4, MOHC-REMO, CCCma-RCA4, IPSL-RCA4, and CNRM-RCA4 are also the best GCM-RCM combinations as observed from strong significant spatial correlation, as well as low bias, RMSE, and positive skill score as high as 0.7. Therefore, the GCM-RCM combinations that exhibit superior performance over EA in most seasons as well as in capturing observed annual mean are CCCMA-RCA4, MIROC-REMO, MPI-REMO, IPSL-RCA4, CSIRO-RCA4, MOHC-REMO, and MIROC-RCA4. The difference in skills between models as well as variation of the same model skill both spatially and seasonally implies the role of several factors such as local topography, vegetation, and surface type as well as robustness of model physics in capturing small scale processes such as mesoscale convection in boreal summer (e.g., over Ethiopian highlands).

区域气候模型（RCM）通常用于缩小粗分辨率的一般环流模型（GCM），以在空间高分辨率的网格上产生气候变量。缩减数据的质量取决于GCM和RCM的技能。在这项研究中，使用10个GCM约束边界并提供三个RCM的初始条件。总共使用18个GCM-RCM组合在东非（EA）上进行模拟。针对气候研究单位（CRU）的降雨，对模拟降雨的准确性进行评估，以确定最佳的GCM-RCM组合。偏差，均方根误差（RMSE），相关系数和基于MAE的模型技能得分均显示MPI-REMO，MIROC-REMO，MPI-RCA4，IPSL-RCA4，CCCMA-RCA4，MOHC-CCLM，MOHC-REMO ，以及春季的CNRM-RCA4；ICHEC-REMO，MIROC-REMO，MOHC-REMO，MIROC-RCA4，CSIRO-RCA4和MPI-REMO在秋季；北方夏季，CSIRO-RCA4，MIROC-RCA4，CCMCA-RCA4，MIROC-REMO，CNRM-RCA4和MOHC-RECA；冬季冬季，ICHEC-REMO，NOAA-RCA4，MOHC-REMO，MOHC-CCLM，MIROC-REMO，MPI-REMO和IPSL-RCA4是表现最佳的GCM-RCM组合。同样明显的是，秋季的模型技巧要优于春季和夏季的模型技巧。此外，EA中的夏季降雨是模型最难模拟的。从CRU降水的年平均值比较表明，MPI-REMO，MIROC-REMO，CSIRO-RCA4，MOHC-REMO，CCCma-RCA4，IPSL-RCA4和CNRM-RCA4也是从强观测到的最佳GCM-RCM组合显着的空间相关性，以及低偏见，RMSE和正技能得分高达0.7。因此，在大多数季节中表现优于EA的GCM-RCM组合以及在捕获的观测到的年平均数上均包括CCCMA-RCA4，MIROC-REMO，MPI-REMO，IPSL-RCA4，CSIRO-RCA4，MOHC-REMO和MIROC- RCA4。模型之间的技能差异以及同一模型的技能在空间和季节上的差异都暗示了多种因素的作用，例如局部地形，植被和表面类型，以及模型物理学在捕获小规模过程（如中尺度）中的鲁棒性北方夏季（例如，在埃塞俄比亚高原上）的对流。