ORIGINAL PAPER Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India Rashid Mahmood & Mukand S. Babel Received: 13 February 2012 / Accepted: 4 September 2012 / Published online: 25 September 2012 # Springer-Verlag 2012 Abstract The study evaluates statistical downscaling mod- el (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum tempera- ture, and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled cli- mate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipita- tion, respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quanti- tative procedure. During validation, average R 2 values by the annual sub-model (SDSM-A)—followed by bias correc- tion using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R 2 values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of the 2020s, 2050s, and 2080s in this basin. Both sub-models show a mean annual increase in max temperature, min temperature, and precipitation. Under H3A2, and according to both sub- models, changes in max temperature, min temperature, and precipitation are projected as 0.91–3.15 °C, 0.93–2.63 °C, and 6–12 %, and under H3B2, the values of change are 0.69–1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s. These results show that the climate of the basin will be warmer and wetter relative to the baseline period. SDSM-A, most of the time, projects higher changes in climate than SDSM-M. It can also be concluded that although SDSM-A performed well in predicting mean annual values, it cannot be used with regard to monthly and seasonal variations, especially in the case of precipita- tion unless correction is applied. 1 Introduction The increasing concentration of greenhouse gases in the at- mosphere due to human activities such as land use changes and the dependence upon fossil fuels has resulted in global warming and a global energy imbalance (Wentz et al. 2007; Chu et al. 2010; Huang et al. 2011). According to the Fourth Assessment Report (4AR) of the Intergovernmental Panel on Climate Change (IPCC), a 0.74 °C rise in the global mean surface temperature was reported in the last hundred years, specifically between 1906 and 2005. Significant increase has been reported in the last 50 years, with an increasing rate of 0.13 °C every 10 years, and the global mean surface temper- ature is projected to increase approximately from 1.1 to 6.4 °C during the twenty-first century (IPCC 2007). This increased global warming can impact the hydrological cycle, affecting water resources, public health, industrial and municipal water demands, water energy exploitation, and the ecosystem (Chu et al. 2010; Zhang et al. 2011). To date, the main tools to predict the variability and changes in climate variables, on global and continental levels, are Global Climate Models that are also called General Circulation Models (GCMs). These advanced and numerical-based coupled models interpret global systems R. Mahmood (*) : M. S. Babel Water Engineering and Management, Asian Institute of Technology, Pathumthani, Thailand e-mail: rashi1254@gmail.com Theor Appl Climatol (2013) 113:27–44 DOI 10.1007/s00704-012-0765-0