Role of Climate Variables for Statistical Downscaling of General Circulation Model Outputs to Precipitation Laddimath Rajashekhar S.*,**, Patil Nagraj S.***, Nataraj M.**** *School of Civil Engineering, REVA University, Bengaluru, Karnataka, India **Research Scholar, Visvesvarayya Technological University, Belagavi, Karnataka, India ***Dept. of Water & Land Management, Center for P.G. Studies, Visvesvarayya Technological University, Belagavi, Karnataka, India ****Junior Research Fellow, Dept. of Water & Land Management, Visvesvarayya Technological University, Belagavi, Karnataka, India Online published on 10 May, 2019. Abstract Impacts of climate change and its consequence has greater influence on the available water resource. Climate change caused by the rise in atmospheric Greenhouse Gases (GHG) has begun to transform life on Earth. It's well accepted phenomenon and also evident to note that, around the world, seasons are shifting, temperatures and sea levels are rising. General Circulation Models (GCMs) are the most advanced, robust tools currently available for the simulation of future climate and weather. GCMs are available at coarse-resolution.and doesn't allow for hydroclimatic predications at the local fine resolution scale and thus, incapable of producing desired outputs at the fine spatial resolution required for studies on hydrology. Downscaling technique address the issue by linking spatial resolution from GCM outputs to hydroclimatic predictors. Present paper explores an approach towards potential selection (sensitivity analysis) of climate variables for statistically downscaling monthly CanCM4 GCM Precipitation (P) outputs to IMD gauge stations located in Bhima basin. In addition a brief discussion on Precipitation downscaling is brought in to link the objectives of the work. As a precursor, inter-variable correlations were investigated within a suite of 22 potential downscaling predictor variables on a monthly time scale for two regions within the Bhima sub basin, and observed correlations were compared with those based on the CanCM4 GCM. The classic techniques product moment correlation, Spearman's rank correlation and Kendall's tau were used for the sensitivity analysis. Indian Meteorological Department (IMD) daily observed precipitation data and the predictor variables were extracted from Canadian Centre for Climate Modeling and Analysis Canada (CCCM). Top Keywords Climate change, GHG, GCM, sensitivity analysis, Statistical Downscaling, and Hydroclimatic predictors/predictands. Top |