A large number of General Circulation Models (GCMs) are currently available for modelling the atmospheric conditions over the Earth. However, there is a large variability in the future climate predicted by the available set of climate models. Hence, the climate data introduces the most amount of uncertainty in the climate change impact assessment. Regional-scale climate change impact studies based on these models may produce a wide range of possible impacts that becomes unusable for policymakers. A robust GCM selection procedure is introduced in the current study to bring the uncertainty to a realistic range. The proposed approach takes into account the process representation in the climate models by checking teleconnections in data along with their ability to predict the regional climate in spatial and temporal scale. The interdependence between the climate models are also accounted for in the proposed approach to avoid underestimation of uncertainty. The procedure is validated in the Bharathapuzha River Basin, Kerala, India. The study considers 22 GCMs that participated in the Coupled Model Inter-comparison Project-5 and 6 Regional Climate Models (RCMs) that are recommended for the Indian subcontinent. The climate models BNU-ESM, CMCC-CM, GFDL-ESM2G, GFDL-ESM2M and MPI-ESM-MR are found to be performing well for the prediction of both precipitation and temperature. The proposed climate model selection procedure can bring down the band width of uncertainty from 376 mm to 162 mm in monthly rainfall prediction with a containing ratio of 44%. The downscaling of the climate predictions can further increase the containing ratio by removing the systematic error. The bandwidth of uncertainty has reduced from 10.82 K to 3.83 K in the prediction of minimum temperature and from 8.35 K to 4.52 K for maximum temperature. The proposed GCM selection procedure provides more confidence in the predicted future climate since regionally significant correlations between climate variables are preserved in the selected models. The model selection procedure is validated for the period 2006-2018 with the observed climatic variables, and the selected models are found to be performing well.