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<p>Reliable seasonal climate forecasts are critical for effective water resource management and disaster preparedness, especially in regions like Iran that experience high climatic variability and extreme precipitation events. This study evaluates the performance of seven models from the Copernicus Climate Change Service (C3S) and their multi-model ensemble (MME) in predicting mean and extreme precipitation indices across Iran. Based on data from 148 synoptic stations across diverse climatic zones (1997–2016), forecast skill at one- to three-month lead times was evaluated using Nash–Sutcliffe Efficiency (NSE) and Pearson correlation (Corr). MME forecasts consistently outperformed individual models across all climate zones and lead times. Among individual models, the ECMWF and UKMO systems achieved the highest skill, especially for short lead times, while other models (e.g. Météo-France, CFSv2) generally performed worse. Skill was notably higher in western and northeastern Iran, whereas forecasts were weakest along the humid Caspian coast. Forecast skill declined as lead time increased. These results quantify the strengths and limitations of current C3S seasonal forecasts for Iran, highlighting that multi-model ensembles and top-performing models (ECMWF/UKMO) offer the most reliable guidance for water resource and risk management.</p>

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Assessment of C3S monthly to seasonal climate forecast models for mean and extreme precipitation over Iran

  • 期刊论文

  • 发表日期：

    2025年12月26日

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  • Mohammad Saeed Najafi,
  • Sajad Akbari Moghaddam Sani,
  • Razieh Noroozian,
  • Samin Danandeh Saribaglou

摘要

Reliable seasonal climate forecasts are critical for effective water resource management and disaster preparedness, especially in regions like Iran that experience high climatic variability and extreme precipitation events. This study evaluates the performance of seven models from the Copernicus Climate Change Service (C3S) and their multi-model ensemble (MME) in predicting mean and extreme precipitation indices across Iran. Based on data from 148 synoptic stations across diverse climatic zones (1997–2016), forecast skill at one- to three-month lead times was evaluated using Nash–Sutcliffe Efficiency (NSE) and Pearson correlation (Corr). MME forecasts consistently outperformed individual models across all climate zones and lead times. Among individual models, the ECMWF and UKMO systems achieved the highest skill, especially for short lead times, while other models (e.g. Météo-France, CFSv2) generally performed worse. Skill was notably higher in western and northeastern Iran, whereas forecasts were weakest along the humid Caspian coast. Forecast skill declined as lead time increased. These results quantify the strengths and limitations of current C3S seasonal forecasts for Iran, highlighting that multi-model ensembles and top-performing models (ECMWF/UKMO) offer the most reliable guidance for water resource and risk management.