نبذة مختصرة : Introduction Climate is one of the most important ecological factors, and its changes are currently the most important threat to sustainable development. The phenomenon of climate change causes different processes in the atmosphere and on the Earth. Phenomena such as rising sea levels, changes in meteorological variables such as temperature and rainfall, impacts on surface currents, occurrence of floods and droughts, and changes in air currents and storms are only part of the effects of climate change. Therefore, it is necessary to model the future conditions of the climate to understand the future conditions. There are various methods for simulating and predicting climate variables in future periods under the influence of climate change, the most reliable of which is the use of General Circulation Model (GCM) data. GCM models are only able to simulate the data of the atmospheric general circulation model at large scales. Even if global climate models are set up with high technical power to predict the future, the need to downscale the results of these models at the station scale remains. Therefore, in this research, the effects of climate change on the threshold values of rainfall and temperature have been evaluated using SSP scenarios. Materials and Methods General circulation models (GCMs) can provide the best information about the response of the atmosphere to increasing greenhouse gas concentrations. In this research, the climatic data of three synoptic stations of Abadeh, Shiraz, and Lar, related to Fars province, were used. The data from three models, ACCESS-ESM1-5, CNRMCM6-1, and MRI-ESM2-0, were used from the general circulation models of the sixth report. Daily rainfall and maximum temperature data from 1990 to 2017 were used. Using the statistical model LARS-WG and three scenarios, SSP126, SSP245, and SSP585, rainfall and maximum temperature have been downscaled. In this model, the process of generating artificial weather data is done in three parts: model calibration, model validation, and weather data generation. To evaluate the LARS-WG model, the coefficient of determination (R2 ), root mean square error (RMSE) test statistics have been used. To investigate the relationship between rainfall and maximum temperature with different return periods, the Gumbel distribution was used. The appropriate distribution for maximum rainfall, temperature, and flood data is Gumbel's method; In this study, the distribution of rainfall and maximum temperature for different return periods is presented. In this method, the mean value and standard deviation of the data and the length of the data return period are considered to be the most important factors affecting in estimating the maximum values. Results and Discussion Validation of the LARS-WG model was done by comparison between observed data and generated data. To evaluate the efficiency of the model, error test criteria have been used. The results show that the LARS-WG model was able to estimate the maximum temperature and rainfall. The accuracy of the modeling in the maximum temperature parameter has been more appropriate than the rainfall parameter. The monthly rainfall changes of the near future period (2021-2040) compared to the base period (1990-2017) of the three ACCESS-ESM1-5, CNRMCM6-1 and MRI-ESM2-0 models of Abadeh synoptic station showed the amount of rainfall in April, May, June, August, and September has had a decreasing trend compared to the base period. The amount of rainfall in January, February, and December has also increased compared to the base period. At Abadeh station, it shows an increase in temperature under all three models and scenarios in the near future. At the Shiraz synoptic station, rainfall in April, July, and September has decreased compared to the base period. The amount of rainfall in January, February, and March has also increased compared to the base period. The maximum temperature has also increased. At the Lar synoptic station, the rainfall in April, September, and October has decreased compared to the base period. The amount of rainfall in January, February, and March has also increased compared to the base period. The maximum temperature has also increased .The Gumbel distribution output also showed that in all three stations, in a specific return period, rainfall and maximum temperature will increase compared to the base period. Examining the Gumbel distribution of rainfall values also shows an increase in rainfall in the specified return period in the ACCESS-ESM1-5 model. Conclusion The changes in the maximum temperature of the near future period (2021-2040) compared to the base period (1990-2017) were incremental in three stations and three models. In Abadeh synoptic station, the maximum temperature changes show an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, respectively of 1.57, 1.59, and 1.63 °C, and the amount of rainfall in the spring and summer seasons is decreasing and winter rainfall is estimated to be increasing compared to the base period. In the Shiraz synoptic station, the maximum temperature shows an increase in the maximum temperature in the three scenarios SSP126, SSP245, and SSP585, 1.37, 1.50, and 1.48 °C, respectively, and in the ACCESS-ESM1-5 model, in all three scenarios, the amount It is estimated that the winter rainfall is decreasing and the amount of spring rainfall is increasing. The changes in the maximum temperature of the Lar synoptic station show an increase in the maximum temperature in the three scenarios, SSP126, SSP245, and SSP585, respectively, 1.23, 1.37, and 1.28 °C. In the CNRM-CM6-1 model, the winter rainfall of this station is estimated to be decreasing. Fall rainfall is also estimated to decrease in the MRI-ESM2-0 model in two scenarios, SSP126 and SSP585, but the ACCESS-ESM1-5 model has estimated an increase in the amount of rainfall in the Lar synoptic station in all seasons and scenarios. The Gumbel distribution output also showed that in all three stations, in a specific return period, rainfall and maximum temperature will increase compared to the base period. Therefore, extreme and heavy rainfall and the increase in the frequency of extreme events related to it, such as floods and droughts, are among the results of global warming. [ABSTRACT FROM AUTHOR]
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