The increasing concentration of greenhouse gases in the atmosphere due to human activities such as land use changes and fossil fuels usage leads to global warming and global energy imbalance. This increase in greenhouse gases cause a phenomenon called climate change. The aim of this study is to evaluate the effect of climate change on surface runoff is Ghara soo basin. In this study, the performance of four GCMs namely HADCM3, CGCM3T63, 5.CSIROMK3, NCARCCSM3 (from the collection of AR4 models) and NCARPCM (from the collection of ATR models) under the A2 scenario is investigated for the simulation of climate parameters such a average temperature and rainfall over Gharh soo basin using artificial neural network (ANN) as a downscaling technique. For training artificial neural network perceptron forward method was used. According to performance evaluation of these models using maximum absolute error, mean absolute error , root-mean-squared error between AR4 and ATR models, AR4 models had better performance than ATR models with less uncertainty in simulating climatic parameters (e.g. rainfall and average temperatures) in Ghara soo basin during 1996-2000. Among the five models listed for AR4 and ATR, NCARCCSM3 has the best performance in simulating temperature parameters and CSIRO MK3.5 has the best performance in simulating precipitation for Gharhsoo basin. The precipitation, maximum and minimum temperature at Ghara soo basin for A2 and B2 scenarios were downscaled from HadCM3 using SDSM model Version 4.2. SDSM calibration results in Gharahsoo basin showed that R 2 (coefficient of determination) were equal to 0.85 and 0.24 for average temperature and rainfall, respectively. These results indicate that SDSM model has adequate efficiency for downscaling temperature but not for rainfall. The 1971-1996 and 1995-2001 periods were selected as the calibration and accuracy assessment, respectively. Results of validation based on Russell Square Quality Representatives indicated that HADCM3 model has a good performance in simulation of climatic parameters in Gharahsoo. This analysis is based on projection of two different scenarios of climate change for future time horizons: (2020-2049) and (2070-2099). The result revealed that the maximum and minimum temperatures increase for two scenarios in all future time horizons. However, precipitation does not show a significant increase or in all future time horizons. For simulating runoff, IHACRES model was used. The input data to this model are temperature, precipitation and daily discharge. For calibrating rainfall-runoff process, temperature, precipitation and discharge data in daily scale entered to IHACRES model for 1971 to 1995 period. For validating IHACRES model 1996-2000 period was considered. After evaluating of the rainfall-runoff model, daily discharge was simulated for 2020-2049 and 2070-2099 periods. The results showed daily stream flow reduction for all stations Compareing observed and simulated daily discharge. Key Words : Climate Change, GCM, SDSM Model, Artificial Neural Network (ANN), HADCM3 Model, Ghara soo Basin