End users of seasonal rainfall forecasts demand not only skilful forecasts of rainfall totals but also skilful forecasts of rainfall extremes. For better forecasts of rainfall extremes, the copula-based postprocessing (CPP) method, which was originally designed for forecasting rainfall totals, is modified with a hybrid probability distribution to model low-to-medium and heavy rainfall separately and to allow the forecast of extreme rainfall events that have never occurred in observed records. A case study for 17 rainfall stations in Queensland, Australia is carried out to test the forecast performance of the modified CPP to postprocess the raw Australian Community Climate and Earth-System Simulator (ACCESS) Seasonal model (ACCESS-S) seasonal rainfall forecast at a daily scale. The modified CPP improves the overall skill of forecasting 12 rainfall indices from the raw forecast and outperforms two quantile mapping based methods in most cases. The use of the hybrid distribution leads to more promising forecast skill for heavy and very heavy rainfall related indices. The forecast skill decreases with longer lead times and the modified CPP leads to neutral forecasts (i.e. forecasts with skill similar to climatology forecasts) for most rainfall indices beyond 0-month lead time. The skill improvement has been found in all selected climate regions and initialisation dates (from the 1st day of each month), though more substantial improvement is observed in the rainfall stations within the tropical zone where the raw forecast is particularly unskilful.

季节性降雨预报的最终用户不仅需要对降雨总量进行熟练的预报，而且还需要对极端降雨进行熟练的预报。为了更好地预测极端降雨，最初设计用于预测降雨总量的基于 copula 的后处理 (CPP) 方法被修改为混合概率分布，以分别模拟低到中等和强降雨，并允许预测观测记录中从未发生过的极端降雨事件。对澳大利亚昆士兰州的 17 个降雨站进行案例研究，以测试修改后的 CPP 的预测性能，以对原始澳大利亚社区气候和地球系统模拟器 (ACCESS) 季节模型 (ACCESS-S) 的季节性降雨预报进行后处理每日规模。修改后的 CPP 提高了从原始预测中预测 12 个降雨指数的整体技能，并且在大多数情况下优于两种基于分位数映射的方法。混合分布的使用为强降雨和特强降雨相关指数提供了更有前景的预测技巧。预测技能随着提前期的延长而降低，修改后的 CPP 导致大多数超过 0 个月提前期的降雨指数的中性预测（即具有类似于气候学预测的技能的预测）。在所有选定的气候区域和初始化日期（从每个月的第一天开始）都发现了技能改进，尽管在原始预报特别不熟练的热带地区的降雨站中观察到了更实质性的改进。