Abstratc:
Fars province is one of the agricultural hubs of the country, due to the dependence of its water resources on annual precipitation, the change of precipitation regime can impact the pattern of cultivation and production in this province. This research intends to investigate and analyze the trend of spatial and temporal changes in precipitation in Fars province during the thirty-year period (1985-2014), using one of the CMIP6 climate models (namely CanESM5) under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios to project the annual and monthly changes of precipitation in the coming decades (2026-2096). To carry out this research, the monthly and annual precipitation of the fars province synoptic stations and the data of the CanESM5 climate model, which is one of the most widely used global atmospheric general circulation model, were used. The results showed that there is a possibility of an increase in the annual precipitation of Fars province in all scenarios; Thus, the SSP126 scenario projected the least increase and the SSP370 scenario predicted the highest increase in precipitation compared to the base period. Accordingly, it was estimated that the increasing trend of projected predicted precipitation will be insignificant under the SSP126 scenario, but will be significant under the other scenarios. Also, the examination of the monthly precipitation regime of Fars province showed that in almost all SSP scenarios, there is a decrease in precipitation in the relatively hot months of the year, i.e. August, September or October, and in the case of the SSP126 and SSP245 scenarios, a decrease in precipitation in the early months of the year (January and February) is also predicted. On the other hand, a probable increase in the spring precipitation of Fars province was predicted, compared to the base period and in all scenarios, as well. The results of this research indicate the necessity of planning to make maximum use of the changes in the monthly precipitation regime of Fars province for spring crops and observing the necessary precautions and measures for autumn crops and considering the possibility of a decrease in autumn precipitation of this province. In addition, it is suggested that future studies utilize a variety of GCM models and different downscaling methods to quantize the uncertainties in precipitation projections arising from three sources: the model, the downscaling method, and the emission scenarios.