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.

准确的降水预报在决策制定中具有很高的社会经济价值，我们提出了一种全球统计后处理方法来处理基于格点的降水集合预报，该方法基于U-Net实现分布回归来预测参数分布，与现有的后处理方法相比，分布回归U-Net在连续分级概率评分方面取得了可比的性能，但在高气候降水区域无法提供校准的预报，而在强降水事件的预测能力方面，它们优于分位数回归森林和带有超尾扩展的半参数分位数回归森林。

降水集合预报的分布回归 U-Net 后处理