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In the study, downscaling models based on artificial neural networks were established for monthly average and maximum temperature and monthly total precipitation projections of Seferihisar, Selcuk and Odemis meteorological stations in the basin. In the models, NCEP/NCAR re-analysis variables were used as predictors. The downscaling models calibrated with the optimum predictors convert the coarse resolution results of both reference period (20C3M; 1981-2010) and future period (A2, A 1B and B1; 2021-2100) scenarios of ECHAM5 climate model to the station scale temperature and rainfall forecasts. Corrections of biases in the forecasts are achieved by using cumulative distribution functions. According to the A2, A1B and B1 scenarios, the mean of monthly average temperatures of 2021-2100 period could increase by 3.2, 3.5 and 2.8 degrees C, respectively and the mean of monthly maximum temperatures of 2021-2100 period could increase by 1.6, 2.1 and 1.1 degrees C, respectively, the mean of annual total precipitation could decrease by 31.6, 42.9 and 30.2%, respectively over study region. Under these possible impacts, it is expected that the average net irrigation water demand and soil salinity will increase, water supply will decrease. Under these stressed conditions, it has to be changed cropping pattern of the basin.