Predicting maximum temperatures using global climate models under RCP scenarios and microscaling LARS-WG and SDSM models in the west of the country

Rising global warming is one of the greatest challenges facing humanity in the 21st century. Therefore, it is very important to predict the maximum temperatures in order to know the amount of changes and, as a result, to take the necessary measures to adapt and moderate the adverse effects caused by it. Therefore, in this study, maximum temperatures were predicted in three provinces of Kurdistan, Kermanshah and Ilam in the west of the country. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios: RCP2.6, RCP4.5 and RCP8.5, as well as two microscale models of LARS-WG and SDSM, and changes in maximum temperatures on a monthly and annual basis in the future 2050-2021) compared to the base period (2018-1989) were examined in 17 meteorological stations. MAE, MSE, RMSE and R2 indices were used to calibrate and validate SDSM and LARS-WG models. The results showed that both models have a high ability to simulate the maximum temperatures of the study area. However, the SDSM model is more accurate than the LARS-WG model, with the lowest and highest accuracy for Bijar and Tazehabad stations with RMSE of 0.02 and 0.18, respectively. The results of maximum temperature forecasting also showed that according to both models, the maximum temperature in the future period will increase compared to the base period, which is the average of the studied models between 0.8 to 1.9 degrees Celsius in the region. Will be studied. The highest rate is estimated based on the RCP8.5 scenario. Spatially, the most changes are related to the northern and eastern areas of the study area and the least changes are related to the western areas of the study area.