General Circulation Models (GCMs) are indispensable tools to project future climate. It is not realistic or necessary to use all GCM datasets when assessing climate risks and building adaptive capacity. Thus, a rational procedure for selecting GCM datasets is needed. It is also required to classify weather stations into climate zones and then suggest a suitable list of GCM datasets to avoid weather stations with similar climate patterns but using different GCM datasets. The purpose of this study is to establish a process for selecting GCM datasets for a region. The process consists of climate zonation, applicability ranking, and a model similarity check. Principal component analysis (PCA) and cluster analysis are used to classify regional weather stations into climate zones. The weighted average ranking (WAR) method and demerit point system (DPS) are then used to rank the GCM performance using CMIP5 (Coupled Model Intercomparison Project Phase 5) datasets. The GCM family tree is then applied to screen out highly similar GCMs before generating a GCM suggestion list. Taiwan is chosen as the study area for this investigation. Taiwan receives monthly mean precipitation data from 25 weather stations. The weather stations were clustered into ten climate zones with different GCM datasets suggested for each zone. The top five GCM datasets suggested for Taiwan by the WAR method are HadGEM2-AO, CESM1-CAM5, CCSM4, MIROC5, and GISS-E2-R while those suggested by the DPS method are CSIRO-Mk3-6-0, HadGEM2-AO, CESM1-CAM5, MIROC5, and CCSM4. The GCM selection process presented in this study is applicable to other regions to assist users in finding GCM datasets suitable for their research.