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Changes in temperature and precipitation patterns have serious impacts on the quantity and quality of water supply, especially in arid regions. In recent years, frequent climatic droughts have threatened the water supply in East Azerbaijan Province, Iran. Because of the increasing demand for water, studying the potential climate change and its impacts on water resources is necessary. To predict the climate change based on the General Circulation Models (GCM), the successful downscaling tool of LARS-WG is applied. This stochastic weather generator downscaled the climate change of six synoptic stations in the province by using the HADCM3 model and three emission scenarios, A1B, A2 and B1, with the horizons 2020, 2055 and 2090. The research outcomes, based on the A2 scenario, show an average annual temperature rise of similar to 2.3 degrees C and an annual precipitation reduction of similar to 3% in the middle of this century. These changes shift the climate of the province from semi-arid to arid based on the De Martonne aridity index. Using the artificial neural network (ANN), a model was then built to simulate the effects of climate change on the runoffs in three watersheds: the results showed dramatic reductions in the flows. The results of this study could advise the designers and managers of this region to take suitable actions in securing the water supply. (C) 2011 Elsevier B.V. All rights reserved.