Statistical Downscaling of Minimum Temperature of Raipur (C.G.) India

The future projected climate parameters obtained from using generalized circulation models (GCMs) cannot be used directly on regional or basin scale because of coarse resolution. The dynamic or statistical downscaling procedures are used to convert global scale output to regional scale condition. The statistical downscaling because of its less computational skills is preferably used for generation of future climate and in the present study, minimum temperature of Raipur was forecasted for three future periods using Canadian Global Climate Model (CGCM) predictors for A1B and A2 climate forcing conditions. The statistical downscaling model (SDSM) has been used using k-fold validation technique for generation of multitemporal series for periods FP-1 (2020–2035), FP-2 (2046–2064), and FP-3 (2081–2100). The specific humidity at 850 hpa (nceps850gl), 500 hpa geopotential height (ncepp500gl), and surface airflow strength (ncep_fgl) were found to be the most appropriate parameters to generate future scenarios. The comparison of mean monthly minimum temperature of generated scenarios with base period confirmed 1.1–11.2% increase of minimum temperature under A1B climate forcing and 2.88–24.44% in summer months will have adverse effect on various demands and human health in future and adaptation measures need to be devised for the region.