Accurate and well-planned forecasts provide critical information for preparedness and mitigation strategies as well as the sustainable practice of water resources conservation. In this paper, an experimental seasonal drought forecasting system has been developed based on meteorological hindcasts, generated by the North American Multi-Model Ensemble (NMME) models. The proposed toolbox comprises (1) NMME data as well as observations, (2) post-processing methods, namely GrandNMME and bias correction methods to statistically post-process precipitation predictions, (3) evaluation metrics of a multi-criteria decision-making method (namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)) to choose the best post-processed improved data, and (4) the Standardized Precipitation Index (SPI) calculator as the central engine, where distribution maps of seasonal drought forecasts are generated. The toolbox has been utilized for the case of Iran. The country is located in semiarid and arid regions of the world, facing considerable water crisis including droughts. Results indicated that the proposed NMME-based drought forecasting toolbox has a significant skill in forecasting droughts over the study area and provides critical information for early warnings, medium-term response planning and taking preventive measures.

准确且计划周密的预报可为备灾和减灾战略以及水资源保护的可持续实践提供重要信息。本文基于北美洲多模式组合（NMME）模型产生的气象后兆，开发了实验性季节性干旱预报系统。拟议的工具箱包括（1）NMME数据和观测值；（2）后处理方法（即GrandNMME）和用于统计后处理降水预测的偏差校正方法；（3）多标准决策方法的评估指标（即通过类似于理想解决方案的偏好排序技术（TOPSIS））来选择最佳的后处理改进数据，以及（4）标准化降水指数（SPI）计算器作为中央引擎，生成季节性干旱预报的分布图。该工具箱已用于伊朗。该国位于世界半干旱和干旱地区，面临着包括干旱在内的严重水危机。结果表明，基于NMME的拟议干旱预报工具箱在预测研究区域的干旱方面具有重要技能，并为预警，中期响应计划和采取预防措施提供了重要信息。