ddae5013-8640-4cd5-adac-abb9c6e6d4d720210714025146508naun:naunmdt@crossref.orgMDT DepositInternational Journal of Energy and Environment2308-100710.46300/91012http://www.naun.org/cms.action?id=3043324202132420211510.46300/91012.2021.15https://www.naun.org/cms.action?id=23309T.DavitashviliIlia Vekua Institute of Applied Mathematics of Ivane Javakhishvili Tbilisi State University, 2 University Street, 0186, Tbilisi, GeorgiaN.KutaladzeNational Environmental Agency of Ministry of Environment Protection and Agriculture of Georgia, 150, David Agmashenebeli Ave., 0112, Tbilisi, GeorgiaR.KvatadzeGeorgian Research and Educational Networking Association GRENA, 4a, Chovelidze Street, 0108, Tbilisi, GeorgiaL.MegrelidzeNational Environmental Agency of Ministry of Environment Protection and Agriculture of Georgia, 150, David Agmashenebeli Ave., 0112, Tbilisi, GeorgiaG.MikuchadzeNational Environmental Agency of Ministry of Environment Protection and Agriculture of Georgia, 150, David Agmashenebeli Ave., 0112, Tbilisi, GeorgiaI.SamkharadzeIvane Javakhishvili Tbilisi State University, 1, Chavchavadze Ave., 0179, Tbilisi, GeorgiaThis study evaluates the ability of several Regional Climate Models (RCMs) to simulate rainfall patterns in the South Caucasus region. In total, 8 RCM simulations were assessed against the CRU observational database over different domains, among them two from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Seasonal climatology, annual rainfall cycles and interannual variability in RCM outputs were estimated for 8 homogeneous sub-regions against several observational datasets. Different metrics covering from monthly and seasonal to annual time scales are analyzed over the region of interest. The results confirm the distinct capabilities of climate models in capturing the local features of the climatic conditions of the South Caucasus region. At the same time, the analysis shows significant deviations in individual models depending on the sub-region and season; however, the ensemble mean is in better agreement with observations than individual models. 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