There is a need to postprocess seasonal rainfall forecasts from physical climate models to reduce bias, improve skill and restore daily variability for use as input for crop simulation at the farm scale. We develop an extended copula postprocessing (ECPP) method to deal with daily rainfall with numerous zero occurrences. By treating rainfall as a left-censored variable, we derive likelihood estimation and adjust simulation procedure with consideration of zero rainfall occurrences. In a case study for 50 representative agricultural stations in Australia, we test our method to postprocess daily rainfall forecasts with up to 186-day lead time. We demonstrate that the ECPP improves the overall forecast skill from raw rainfall forecasts and outperforms quantile mapping (QM) by checking various verification measures. Though the forecasts for daily amounts are hardly skilful except for the first few days, the forecasts for accumulated totals can be skilful from error averaging and propagating positive skill from short lead times. We also demonstrate that the ECPP can simulate rainfall forecasts with more realistic dry day distribution and daily rainfall intensity than QM. Further research directions including several opportunities to improve ECPP are discussed.

需要从物理气候模型中对季节性降雨预报进行后处理，以减少偏差，提高技能并恢复日常变化，以用作农场规模的作物模拟输入。我们开发了扩展的copula后处理（ECPP）方法来处理每天发生零次降雨的情况。通过将降雨视为左删失变量，我们得出了似然估计并在考虑零降雨发生的情况下调整了模拟过程。在针对澳大利亚50个有代表性的农业站点的案例研究中，我们测试了我们的方法来对每日降雨量预报进行后处理，最多可以提前186天。我们证明，ECPP通过检查各种验证措施，可以提高原始降雨预报的整体预报能力，并且胜过分位数映射（QM）。尽管除了前几天以外，对每日金额的预测几乎不熟练，但是对累积总数的预测可能会因错误平均而有所欠缺，而由于提前期短而会传播正技能。我们还证明，与QM相比，ECPP可以模拟更实际的干旱日分布和每日降雨强度的降雨预报。讨论了进一步的研究方向，包括改善ECPP的几种机会。