Projection the extreme temperatures of Iran's cold season using a multi-model Hamadi system with socio-economic scenarios of CMIP6 models.

Modeling extreme temperatures is one of the important challenges in predicting climate behavior. The aim of the current research is to introduce and evaluate a new Hamadi (weighted mean-correlation) in forecasting the extreme temperatures of the cold season of Iran using socio-economic scenarios. In this regard, the output of 5 models GFDL-ESM4, MPI-ESM1-2-HR, IPSL-CM6A-LR, MRI-ESM2 and UKESM1-0-LL and the data of 95 synoptic stations in two basic periods (1981-2019) and The forecast period (2021-2040) was used. First, the direct output of the models was corrected by the variance method, and then Taylor's diagram and statistical measures were used to check the efficiency of the models and the introduced Hamadi model (weighted average-correlation). Using the newly introduced Hamadi model, limit temperatures were predicted with two optimistic scenarios SSP126 and pessimistic SSP585. The results showed that the error rate of the used Hammadi model has been able to reduce the error of the models used in forecasting to an optimal extent. The estimated data for the minimum temperature are more similar to the actual data than the maximum temperature. The minimum temperature will increase significantly compared to the maximum temperature in the base period. The country's latitude will play a major role in the country's maximum temperature distribution, and the minimum temperature distribution will also be influenced by the country's major altitudes and latitude.