ORIGINAL CONTRIBUTION Climate Change Assessment of Precipitation in Tandula Reservoir System Rahul Kumar Jaiswal 1 • H. L. Tiwari 2 • A. K. Lohani 3 Received: 25 January 2016 / Accepted: 15 January 2018 Ó The Institution of Engineers (India) 2018 Abstract The precipitation is the principle input of hydro- logical cycle affect availability of water in spatial and tem- poral scale of basin due to widely accepted climate change. The present study deals with the statistical downscaling using Statistical Down Scaling Model for rainfall of five rain gauge stations (Ambagarh, Bhanpura, Balod, Chamra and Gondli) in Tandula, Kharkhara and Gondli reservoirs of Chhattisgarh state of India to forecast future rainfall in three different periods under SRES A1B and A2 climatic forcing conditions. In the analysis, twenty-six climatic variables obtained from National Centers for Environmental Predic- tion were used and statistically tested for selection of best-fit predictors. The conditional process based statistical corre- lation was used to evolve multiple linear relations in cali- bration for period of 1981–1995 was tested with independent data of 1996–2003 for validation. The developed relations were further used to predict future rainfall scenarios for three different periods 2020–2035 (FP-1), 2046–2064 (FP-2) and 2081–2100 (FP-3) and compared with monthly rainfalls during base period (1981–2003) for individual station and all three reservoir catchments. From the analysis, it has been found that most of the rain gauge stations and all three reservoir catchments may receive significant less rainfall in future. The Thiessen polygon based annual and seasonal rainfall for different catchments confirmed a reduction of seasonal rainfall from 5.1 to 14.1% in Tandula reservoir, 11–19.2% in Kharkhara reservoir and 15.1–23.8% in Gondli reservoir. The Gondli reservoir may be affected the most in term of water availability in future prediction periods. Keywords Climate change Á Generalized circulation model (GCM) Á Regional circulation model (RCM) Á Downscaling Á Predictor Á Predictand Introduction Different reports of Intergovernmental Panel on Climate Changes (IPCC) [1, 2] and other independent researches has confirmed that climate is changing on global and regional scale which likely to affect availability and supplies of water [3–8] and Water quality [9, 10]. The generation of different scenarios of future climates and development of adaptation plan to cope up the situation are the most important areas of research in the field of climate change. For simulation of future climate on the earth, General Circulation Models (GCMs) are the most advanced tools used in different hydrologic and other studies [11]. These models are capable of predicting climate, hundreds of years into future consid- ering the future probable green house gas (GHG) concen- trations in the atmosphere under different development conditions. The GCMs are the most credible tools designed to simulate time series of climate variables, considering the concentrations of GHGs [12] but unable to resolve signifi- cant sub-grid scale features, including topography and land use, as needed in hydrologic modeling and impact assess- ment analysis [13–16]. The problem of coarse grid data can be solved by downscaling GCMs to local and basin scale with the help of dynamic or statistical downscaling tech- niques that bridge the large scale atmospheric conditions with local scale climatic data [17–20]. & Rahul Kumar Jaiswal rkjaiswal_sagar@yahoo.co.in 1 National Institute of Hydrology, Central India Hydrology Regional Centre, Bhopal, India 2 Department of Civil Engineering, National Institute of Technology, Bhopal, India 3 National Institute of Hydrology, Roorkee, India 123 J. Inst. Eng. India Ser. A https://doi.org/10.1007/s40030-018-0269-8