Improvement of rainfall forecasts on seasonal to within-season timescales is crucial for many vulnerable regions and nations, including Bangladesh. For South Asia, seasonal predictability of rainfall can be quite challenging, and Bangladesh has limited predictive skill with respect to total seasonal rainfall due to its weak relationship with ENSO variability. The relationship between total seasonal monsoon rainfall from June through September (JJAS), as simulated by the North American Multimodel Ensemble (NMME), and within-season observed rainfall statistics for Bangladesh were explored, employing 25-year cross-validations at lead times up to 3 months. The model hindcasts of total JJAS rainfall demonstrate only low-to-modest skill at predicting total observed seasonal rainfall, but a more robust predictive relationship is found for the number of observed dry and wet spells within the season, more so than with the number of extreme dry or wet days. A small ensemble of NMME models could be used to provide more valuable information to aid decision-makers than previously thought, with important implications for agricultural decision-making and climate services.

对于包括孟加拉国在内的许多脆弱地区和国家来说，改善季节到季节内的降雨预报至关重要。对于南亚而言，降雨的季节可预测性可能非常具有挑战性，而孟加拉国由于与ENSO变异性之间的关系较弱，因此在季节总降雨量方面的预测能力有限。利用北美多模式合奏团（NMME）模拟的6月至9月的季节性季风总降雨量（JJAS）与孟加拉国季节内观测到的降雨统计数据之间的关系，并采用了提前25年的交叉验证方法到3个月。JJAS总降雨量的模型后验结果表明，在预测总观测季节降雨量方面只有低到中等的技巧，但是对于季节内观察到的干燥和潮湿天气的数量，发现的预测关系更强，与极端干燥或潮湿天数的关系更是如此。一小部分NMME模型可用于提供比以前认为的更多有价值的信息，以帮助决策者，这对农业决策和气候服务具有重要意义。