Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems. Recently, the Copernicus Climate Change Service (C3S) database has been releasing monthly forecasts for lead times of up to three months for public use. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for one- to three-month lead times. Probabilistic and non-probabilistic criteria were used for evaluation. Furthermore, the skill of selected models was analyzed in dry and wet periods in different precipitation clusters. The results indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons. Moreover, with increasing lead time, the forecast skill of the models worsened. In terms of forecasting in dry and wet years, the forecasts of the models were generally close to observations, albeit they underestimated several severe dry periods and overestimated a few wet periods. Moreover, the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models. In general, the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran. For the clusters considered in Iran and for the long-range system versions considered, the Météo France model had lower skill than the other models.

中文翻译：

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伊朗哥白尼气候变化服务季节性集合降水预报的技能评估

中长期降水预报在水资源管理和预警系统的发展中起着举足轻重的作用。最近，哥白尼气候变化服务 (C3S) 数据库发布了长达三个月的交付周期的月度预测，供公众使用。本研究评估了 1993 年至 2017 年期间伊朗八个分类降水集群中三个 C3S 模型的集合预报，其提前期为一到三个月。概率和非概率标准用于评估。此外，在不同降水集群的干湿期分析了所选模型的技能。结果表明，这些模型在西部降水集群中表现最好，而在北部潮湿集群中，模型的技能得分为负。所有模型都能更好地预测旱季的上三分位数事件和雨季的下三分位数事件。此外，随着提前期的增加，模型的预测能力变差。在干湿年预报方面，模型的预报总体上接近于观测值，尽管它们低估了几个严重的干旱期，并高估了几个湿润期。此外，通过对三个模型的预测进行多元回归生成的多模型预测与单个模型的预测结果相比产生了更好的结果。总的来说，ECMWF 和 UKMO 模型被发现适用于伊朗大多数集群的提前一个月降水预报。对于伊朗考虑的集群和考虑的远程系统版本，