In this study, development of a planning support system (PSS) was studied as a tool to support the planners for analyzing and choosing the best policy instruments in order to adapt to climate change. In order to carry out this study, different methods for regenerating the time series of climatic variables were assessed and finally, a comprehensive weather generator model was developed which was capable in regeneration of statistical characteristics and also spatial correlation between neighboring stations. After downscaling the monthly outputs of GCM models using the Inverse Distance Weighting (IDW) method, the developed weather generator model was used to generate daily time series for the base and climate change scenarios. In order to simulate the Taleghan reservoir daily inflow using the data fusion method, the outputs of the best Artificial Neural Networks and Hammerstein-Wiener models were used and the reservoir daily inflow was simulated under climate change scenarios. The results confirmed the decrease of mean daily inflow in almost all the months. PSS biophysical input coefficients were estimated using the DSSAT crop simulation models under all climate scenarios for 24 land units. The potential production of all studied crops could vary between 86% and 122% of the potential productions in the base scenario. Also, it was revealed that the net irrigation requirement of the crops will decrease by 12% on average. The main goal of the PSS was to maximize the total net income of the entire area. Analyzing the management scenarios using the planning model showed that the best option for climate change adaptation will be the combination of all the instruments in one management scenario. Also, it was concluded that the reduction of interest rate and using two different water prices for surface and pressurized irrigation systems could be the best management scenarios after the combination scenario.