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In this paper, downscaling models are developed using a Multiple Linear Regression (MLR) for obtaining projections of mean monthly temperature and precipitation for Jhelum river basin. Precipitation and temperature data are the most frequently used forcing terms in hydrological models. However, the available General Circulation Models (GCMs), which are widely used nowadays to simulate future climate scenarios, do not provide those variables to the need of the models. The purpose of this study is therefore, to apply a statistical downscaling method and assess its strength in reproducing current climate and project future climate. Regression based downscaling technique was usedtodownscaletheCGCM3, HadCM3 and Echam5 GCMpredictionsoftheA1B scenario for the Jhelum river basin located in India. The Multiple Linear Regression (MLR) model shows an increasing trend in temperature in the study area until the end of the 21 st century. The average annual temperature showed an increase of 2.37°, 1.50°C and 2.02°C respectively for CGCM3, HadCM3 and Echam5 models over 21 st century under A1B scenario. The total annual precipitation decreased by 30.27%, 30.58°C and 36.53% respectively for CGCM3, HadCM3 and Echam5 models over 21 st century in A1B scenario using MLR technique. The performance of the linear multiple regression models was evaluated based on several statistical performance indicators.

Projection of future Temperature and Precipitation for Jhelum river basin in India using Multiple Linear Regression.