Assessment of Pooled CMIP6 Climate Models for estimation of Intensity-Frequency of 24-hour precipitation in Iran

Intensity-Duration-Frequency (IDF) curves are crucial in the design of water and hydraulic infrastructures. These curves are estimated based on rainfall data collected by rain gauge stations. However, due to global climate change, the intensity and frequency of extreme events can be altered. Hence, the existing IDF curves may not be reliable for the design of infrastructures and should be revised based on more recent data. Climate models can be used to investigate the impact of climate change on IDF curves. Typically, after developing the IDF curves from each climate model, the final curve used is extracted based on the median of the data. This study explores the use of five CMIP6 climate models to estimate the intensity-frequency curves of 24-hour rainfall at 12 stations in Iran. In addition to evaluating each model individually, this study also assesses the combined output of the models and compares its results with the curve obtained from the median of the data. Evaluation metrics such as mean error (ME), root mean square error (RMSE) and relative error (RE) indicated that among the studied models, the CMCC-CM2-SR5 model provides a better estimate of the 24-hour rainfall intensity at most stations, likely due to its finer resolution. Furthermore, pooling the models and estimating the IDF curve with the pooled data yielded better results than the median method and most of the individual models, particularly for longer return periods. This suggests that using pooled data from multiple models could improve the accuracy of IDF curve estimates