Assessment of LARS-WG and Change Factor Downscaling Models in Simulating Climate Variables (Case study: Golmakan station)

Today, climate change caused by the increase in greenhouse gases is considered one of the important global issues and has led to anomalies in the global climate system. Downscaling methods play a fundamental role in improving the accuracy of General Circulation model outputs (GCMs). Among these, statistical downscaling methods have more efficiency due to their easy and inexpensive calculations compared to dynamic downscaling methods and are used more. In this study, the results of two statistical downscaling models, LARS-WG and Change Factor (CF) or Delta, in simulating temperature and precipitation parameters under three emissions scenarios (RCP2.6, RCP4.5, and RCP8.5) shortly period (2021-2040) were considered based on observational data from the Golmakan synoptic station in the base period (1975-2005). To evaluate the accuracy of the mentioned methods in estimating the variables, statistical evaluation criteria such as Nash-Sutcliffe efficiency, Root Mean Square Error, and correlation coefficient were used. Ultimately, based on the research findings, the LARS-WG model showed less error in simulating minimum and maximum daily temperature and precipitation in the study area and performed better compared to the Change Factor method in climate prediction.