Statistical downscaling is the technique of linking large-scale predictors and local scale predictand through a relationship that is assumed to be useful to generate local scale climate change information from GCMs driven large-scale future projection. An attempt has been made to construct downscaled seasonal and annual rainfall change scenarios over different station locations of the Western Himalaya Region (WHR) of India using common predictors from ten Global Climate Model (GCM) of CMIP5 (Coupled Model intercomparison Project phase 5) and reanalysis datasets from NCEP/NCAR datasets ( National Centers for Environmental Prediction/National Center for Atmospheric Research) ; and predictands from the IMD (India Meteorological Department) rain gauge stations. Combined EOF (Empirical Orthogonal Function) approach has been used to develop stations specific statistical downscaling models over the WHR and later on some statistical skill scores based on error and agreement analysis were used to validate the model performance. Downscaled precipitation scenario using multi model ensemble of GCM under RCP4.5 (Representative concentration pathways 4.5) revealed a wetter climate during the 2020s, 2050s and 2080s in the annual and monsoon time scale, whereas a drier climate is expected in the winter season. Results reveal a possible intensification of south-west monsoon and decrease in the frequency of western disturbances in the 21 st century as the percentage changes of rainfall in monsoon were higher compared to annual and winter timescale. The uncertainty in the monthly precipitation is predicted to increase as the time progresses during 2020s to 2080s. Higher uncertainty in precipitation is expected in the late pre-monsoon months and early post-monsoon month over the study region.