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Climate change and its impacts on hydrometeorological variables and surface runoff have been demonstrated in many investigations around the world. General circulation models (GCMs) are widely used in climate change studies; however, their applications are limited because of low resolution for regional investigations. Different downscaling models have been developed to overcome this shortcoming, but most of them cannot preserve the monthly characteristics of rainfall that are mostly important in water resources planning and management. In this study, a self-organizing Gaussian-based downscaling (SOGDS) scheme is proposed to preserve the monthly characteristics of rainfall variations. The Gaussian mixture distribution (GMD) was employed to determine the monthly rainfall class based on observed climatic predictors. The monthly characteristics of rainfall were estimated, including the number of dry …