In the area of a vast country like Iran, which is spread in various latitudes and longitudes, it is exposed to different weather conditions. Seasonal temperature changes can be predicted by models. Nowadays, one of the important and key topics is the change of climate behavior. Among the different climatic elements, temperature is of particular importance due to its wide influence on other factors and especially the effects they have on human activities. Temperature is one of the few elements of climate that has continuity over time and can be measured continuously in all places and geographical spaces. This change in climate has direct relationships with other elements of climate such as radiation, relative humidity, wind and rainfall. and has an indirect effect and controls weather processes. It can be seen from researches such as Malekian et al. (2003), Najafi and Dehban (2002), Soleimani Sardo and Mesbahzadeh (2019), Jamali and Khorani (2015), Shah Noorian et al. (2011) reached the common conclusion that the temperature is The number of frosty days is increasing and decreasing, and the region is moving towards climatic hazards. At present, knowing about the prediction of seasonal changes in temperature and the behavior of climatic variables, including temperature, in order to apply the necessary measures against the effects of climate change, has been discussed and the focus of attention of many researchers, especially climatologists. Recognizing and evaluating seasonal temperature changes in the coming decades with the aim of environmental planning in order to adapt to future climate conditions and reduce its effects is an effective matter. In this research, the purpose of the research is to look at the reproduction of the climatic variable of temperature and compare the seasonal changes of temperature in the southwestern stations of Iran with an emphasis on Khuzestan province. The area studied in the current research is Khuzestan province. Khuzestan province with an area of about 63213 square kilometers and relatively 3.9% of the total area of the country, between 47 degrees and 41 minutes to 50 degrees and 39 minutes east longitude from the Greenwich meridian and 29 degrees and 58 minutes to 33 degrees and 4 minutes north latitude from the line Equator is located in the southwest of Iran. In this research, SDSM software and CanESM2 model data were used for statistical scaling. The data used in this research include minimum and maximum temperature, average daily temperature. The base period is based on the available data of all stations from 1961 to 2005. The statistical years from 1991 to 2005 were used to validate the model. The rapid process of climate changes and changes in the behavior of climate variables in recent decades, either due to natural factors or due to industrial activities, has caused a lot of attention to be paid to its forecasting. Considering the effect of temperature on the climatic conditions of each region and the importance of forecasting It has wide applications in environmental planning using atmospheric general circulation models and temperature forecasting and investigating seasonal temperature changes. In this research, in order to compare the seasonal changes of temperature in the southwestern stations of Iran, with an emphasis on Khuzestan province, SDSM statistical microscale model has been used using the outputs of the CanESM2 climate model, under RCP2.6 and RCP8.5 scenarios. 7 synoptic stations of Khuzestan province, which had 45-year (1961-2005) and 40-year (1966-2005) climate statistics, were selected. The data of the base period (1961-2005) is from the first 30 years of data (1961-1990) for calibration and from the second 15 years (1991-2005) for syntactic evaluation of model performance using error and accuracy criteria.

Comparing the results of the statistical analysis for both observational and microscale data sets shows that the SDSM model has a high efficiency in the microscale of CanESM2 model output temperature; So that the amount of RMSE and R coefficients during the verification period in all investigated stations ranged from 1.6 to 2.7 respectively and for the correlation coefficient from 0.989 to 0.997. Also, from the NCEP data, which includes 26 atmospheric variables, which are used as independent variables, and after statistical tests, predictive variables are selected from among them. Considering that predictive variables can have different relationships with predictive data. Therefore, variables with the highest correlation coefficient and the lowest error variance are important

The results showed that the correlation between the average station temperature and NCEP two-meter average temperature is the highest in autumn and the lowest in summer. The average temperature of the stations in the future period compared to the base period in the summer season shows the highest increase compared to other seasons. Examining the frequency index of freezing days showed that there was a decreasing trend in all study stations.