عنوان مقاله [English]
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. In the present study the performance of four GCMs namely HADCM3, CGCM3T63, 5.CSIROMK3, NCARCCSM3 (from the collection of AR4 models) under the A2 scenario is investigated for the simulation of climate parameters such as average temperature and precipitation over Gharhsoo basin using artificial neural network (ANN) as a downscaling technique. neural networks are adjusted, or trained, so that a particular input leads to a specific target output Laminated prspetron model (MLP) is one of the most artificial neural network model of the application of artificial intelligence component in the field of atmospheric and climatic elements forecast that can be regardless of complex non-linear equations Commonly. In Gharhsoo basin during 1996-2000 between the four models listed for AR4, NCARCCSM3 has the best performance in simulating temperature parameters and CGCM3T63 has the best performance in simulating precipitation for Gharhsoo basin Also, the results showed that CSIROMK 3.5 and CGCMT63 models have the most differences with observed climate parameters precipitation and average temperatures respectively According neural network regression coefficient for the two climatic parameters as temperature and precipitation, on average0/97and 0/73 respectively to total area. The mean temperature data simulated for the A2 scenario Better correlation with the observed data In comparison with precipitation data.