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This study has developed and applied a planning support system (PSS) – a tool for planners to analyze and choose the best policy instrument in order to adapt to climate change in the Qazvin irrigation and drainage network, located in the central part of Iran that is mainly supplied by the Taleghan reservoir. A comprehensive weather generator was developed that was capable of regenerating statistical characteristics and linear correlation between neighboring stations. After downscaling monthly outputs from General Circulation Models (GCMs) using the Inverse Distance Weighting (IDW) interpolation method, the weather generator was used to generate daily time series for the base case and projected climate change scenarios. This study simulated the Taleghan reservoir daily inflow under projected climate change scenarios using the data fusion method where outputs from the most representative Artificial Neural Networks and Hammerstein–Wiener models were “fused” to simulate the reservoir daily inflow. Results showed a decrease in mean daily inflow in almost all months. Biophysical input coefficients were estimated using the Decision Support System for Agrotechnology Transfer (DSSAT) crop models under all climate scenarios. The projected production of all studied crops can vary between 86% and 122% of the potential production under the base-case scenario. In addition, it was revealed that the net irrigation requirement for crops will decrease by 12% on an average. The main goal of the PSS was to maximize the total net income for the region. It can be concluded that reducing bank loan interest rate and setting two different water prices for surface and pressurized irrigation systems can be seen as the best management practices in the region.