علی آذره

Climate change is found to be the most important global issue in the 21st century, so to monitor its trend is of great importance. Atmospheric General Circulation Models because of their large scale computational grid are not able to predict climatic parameters on a point scale, so small scale methods should be adapted. Among downscaling methods, statistical methods are used as they are easy to run. Two famous models, ClimGen and SDSM, were studied for daily total precipitation and temperature data in Qazvin station. For this purpose, three steps of models calibration, verification and simulation, in Qazvin station were performed and model performances in terms of similarities in produced data with those using parameters such as root mean square error (RMSE), coefficient of determination (R2) and Nash coefficient (NSE) were assessed. The results in climatic range showed that Climgen outperform in rainfall data generation while SDSM outperforms in simulating average temperatures. However, both models have high potential to simulate temperature and precipitation.