AbstractWestern Disturbances (WDs) are the primary contributor of winter precipitation over the Western Himalayas (WH). The precipitation falling as snow is essential for Himalayan glaciers accumulation, contributing to Himalayan rivers supporting downstream population of the Indian subcontinent. Climate change has affected the Himalayas, which is noticeable through lesser snowfall and higher melt leading to critical water resource stresses and managements. Available climate models are one of the best available tools to assess precipitation changes over and around the Himalayas. Inherent uncertainties and errors in these numerical models need a certain urge for improvement. Integration of statistical models and methods on top of dynamical models provide better insight and utility in associated processes. Thus, a statistical relationship between various predictors and precipitation over the region is investigated. Available observations from APHRODITE and ERA‐I and model fields from EC‐EARTH‐RCA4 (a CORDEX‐SA member) is used. Multiple linear regression (MLR) model is used to establish statistical estimates using meteorological variables as predictors and precipitation. The correlation between the predictors and precipitation is determined, and a contour depicting maximum correlation is used to estimate regression coefficients. The MLR model is trained during present (1987–2005) and tested during 2006–2007. Higher positive correlations between observations and MLR model are found. This developed MLR model is then implemented for assessing future WDs under RCP8.5 scenario. The intensity and frequency of WDs are calculated during the present and future. Model‐based WDs' intensities slightly deviate from the corresponding observations in present. But the model‐based WDs' frequencies match with the corresponding observations in present. The results indicate that WDs show a decreasing trend in frequency and intensity in the present and near‐future. But in far‐future intensity of WDs shows an increasing trend.