Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global statistical postprocessing method for grid-based precipitation ensemble forecasts. This U-Net-based distributional regression method predicts marginal distributions in the form of parametric distributions inferred by scoring rule minimization. Distributional regression U-Nets are compared to state-of-the-art postprocessing methods for daily 21-h forecasts of 3-h accumulated precipitation over the South of France. Training data comes from the M\'et\'eo-France weather model AROME-EPS and spans 3 years. A practical challenge appears when consistent data or reforecasts are not available. Distributional regression U-Nets compete favorably with the raw ensemble. In terms of continuous ranked probability score, they reach a performance comparable to quantile regression forests (QRF). However, they are unable to provide calibrated forecasts in areas associated with high climatological precipitation. In terms of predictive power for heavy precipitation events, they outperform both QRF and semi-parametric QRF with tail extensions.

翻译：精确的降水预报因其在交通网络、农业生产等多个领域决策支持中的重要作用而具有显著的社会经济价值。本文提出一种面向网格化降水集合预报的全局统计后处理方法。该基于U-Net的分布回归方法通过评分规则最小化推断参数化分布形式，从而预测边缘分布。本研究将分布回归U-Net与最先进的后处理方法进行比较，针对法国南部地区每日21小时预报时段内3小时累积降水进行验证。训练数据来源于法国气象局AROME-EPS天气模式，时间跨度为三年。当缺乏连续数据或回算预报时，该方法面临实际应用挑战。实验表明，分布回归U-Net相较于原始集合预报具有竞争优势，在连续分级概率评分指标上达到与分位数回归森林(QRF)相当的性能水平。然而，该方法在高气候降水特征区域未能提供校准良好的预报结果。在强降水事件的预测能力方面，其表现优于标准QRF及带尾部扩展的半参数化QRF方法。