Abstract Poor forecasting of climate variables at river basin scale, leads to situations which entail considerable risks and losses in various sectors such as the agriculture, the environment and water resources. Lack of accurate sub-seasonal and seasonal precipitation predictions are major management and operation predicaments in the transboundary Sistan Basin, located in Afghanistan and Iran, and selected as the case study in this paper. The outputs of the North-American Multi Model Ensemble (NMME), as a seasonal forecasting system, were utilized in the target region. In order to evaluate the performance of the raw outputs of four NMME models (namely: NCEP-CFSv2, CMC-CanCM3, CMC2-CanCM4, NCAR-CCSM4), the individual model ensemble mean was compared to the observations. Results highlighted the need for post-processing of the NMME outputs. For this purpose, three different commonly used methods, including GrandNMME, bias correction quantile mapping (QM) and Copula approaches, alongside a novel hybrid technique, were applied to improve future predictions of precipitation patterns for the hindcast period of 1982–2010 and forecast period of 2012–2016. All four methods were performed for each month in each 0.5-degree cell over the following one to six months. The findings demonstrated the performance of different NMME models were not the same over time and space. Among the models, the NCEP-CFSv2 and CMC2-CanCM4 seemed to best capture the spatial variability of precipitation in the study area. In addition, they performed better in estimating the amounts of precipitation. Among the methods, the QM, the Copula and the hybrid were all successful in improving the geographic patterns of the data. Furthermore, they were all able to enhance the accuracy of the data. At different timescales, the hybrid method showed substantial improvements over the seasonal predictions. Overall, the findings illustrated that collective use of methods may narrow the potential uncertainty in projections of regional changes.

中文翻译：

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改进锡斯坦盆地北美多模式集合 (NMME) 的降水预报

摘要 流域尺度气候变量预测不力，导致农业、环境、水资源等各部门面临相当大的风险和损失。缺乏准确的亚季节性和季节性降水预测是位于阿富汗和伊朗的跨界锡斯坦盆地的主要管理和运营困境，并被选为本文的案例研究。北美多模型集合 (NMME) 的输出作为季节性预测系统被用于目标区域。为了评估四个 NMME 模型（即：NCEP-CFSv2、CMC-CanCM3、CMC2-CanCM4、NCAR-CCSM4）的原始输出的性能，将单个模型集成平均值与观测值进行比较。结果强调需要对 NMME 输出进行后处理。为此，三种不同的常用方法，包括 GrandNMME、偏差校正分位数映射 (QM) 和 Copula 方法，以及一种新的混合技​​术，被应用于改进对 1982-2010 年后报期和预测期降水模式的未来预测2012-2016 年。在接下来的一到六个月内，每个月在每个 0.5 度的单元中执行所有四种方法。研究结果表明，不同 NMME 模型的性能在时间和空间上并不相同。在这些模型中，NCEP-CFSv2 和 CMC2-CanCM4 似乎能最好地捕捉研究区降水的空间变异性。此外，他们在估计降水量方面表现更好。在这些方法中，QM、Copula 和 Hybrid 都成功地改善了数据的地理模式。此外，他们都能够提高数据的准确性。在不同的时间尺度上，混合方法显示出对季节性预测的实质性改进。总体而言，研究结果表明，方法的集体使用可能会缩小区域变化预测的潜在不确定性。