Projection of Change in the Distribution of Precipitation and Temperature Using Bias-Corrected Simulations of CMIP6 Climate Models (Case Study: Kerman Synoptic Station)

In this research, the effects of climate change on the amount and distribution of precipitation and temperature in the Kerman synoptic station were investigated. For this purpose, the output of global climate models of CMIP6 for the historical period (1965 to 2014) for precipitation and temperature were evaluated in comparison with the station data. To evaluate the models, evaluation metrics including correlation coefficient (r), root mean square error (RMSE), mean error (ME) and KGE index were used. Then the best-performed models were selected to predict these two variables in the future period (2051 to 2100) based on different climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Finally, changes in the distribution of precipitation and temperature in the future period compared to the base period were investigated. The results showed that after bias correction, the ACCESS-CM2 and MRI-ESM2-0 models performed more accurately for temperature (ME=0 °C, RMSE=1.87 °C, r=1, KGE=0.998) and precipitation (ME=-0.002 mm/month, RMSE=17 mm/month, r=0.484, KGE=0.468) estimation, respectively. The results of trend analysis indicated the trends in the amount of precipitation were not significant (P-value>0.1). Comparison between the average and variance of precipitation also was not significant (P-value<0.05), and therefore the possibility of increasing the occurrence of extreme precipitation cannot be statistically expected. However, trends in temperature and its average and variance are statistically significant and the possibility of heat stress will increase in the future. A significant increase in temperature in the future can affect the quantity and quality of Kerman's water resources, requiring more attention to the proper management of water resources.