In the study of climate changes, Prediction of the future climatic parameters is performed by general circulation models (GCMs) and emissions scenarios of greenhouse gases. However, Global Circulation Models have very course spatial resolutions. For this reason, downscaling methods are needed to bridge the gap between the large scale climate scenarios and the fine scale where local stations exist. Downscaling methods are divided into two categories: 1) statistical models and 2) Dynamic models. Among these methods, statistical methods are much more popular which is due to low expenses and less time consuming procedures. Lars-WG and SDSM models are among the most concise methods of statistical tools for downscaling. Herein this research these two models were used in simulating precipitation and temperature changes in Urmia lake basin located in the north west of Iran. Four synoptic stations including Saqez, Tabriz Khoy and Urmia were considered. These four stations had a good and long data especially in base period (1961-1990). In order to assess the models, MSE, RMSE & MAE indexes along with regression and bias were used. Results show that both models were good in simulating temperature but SDSM was better in simulating precipitation according to statistical performance measures and has less uncertainty. But it has more complex and time consuming procedures. While Lars-WG is simpler and faster comparing with SDSM. In general, none of the models has absolute superior in