Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.

This study investigates the future changes in precipitation extreme indices in the Lake Urmia Basin during the period 2021–2100 compared to the base period (1987–2016), using the Coupled Model Intercomparison Project Phase 5 models. Trend analysis was performed using Mann–Kendall test and Sen's estimator. The output of these models was downscaled by the Long Ashton Research Station Weather Generator method for the representative concentration pathway (RCPs) 4.5 and 8.5. A model averaging technique was employed to create an ensemble model. The results showed that the average precipitation of the basin will decrease by the end of the 21st century. The projection also showed that the consecutive dry day's index increases based on both scenarios. However, other indices (maximum 1-day precipitation, maximum 5-day precipitation, very wet days, consecutive wet days, simple daily intensity index, and wet-day precipitation) are reduced compared to the base period. Moreover, the slope of significant trends in the RCP8.5 is greater and more severe than that in RCP4.5.