Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) approach, which generates probabilistic forecasts with a parametric distribution whose parameters depend on (statistics of) the ensemble prediction. A case study with daily precipitation predictions across Switzerland highlights that postprocessing at observation locations indeed improves high-resolution ensemble forecasts, with \(4.5\%\) CRPS reduction on average in the case of a lead time of 1 day. Our main aim is to achieve such an improvement without binding the model to stations, by leveraging topographical covariates. Specifically, regression coefficients are estimated by weighting the training data in relation to the topographical similarity between their station of origin and the prediction location. In our case study, this approach is found to reproduce the performance of the local model without using local historical data for calibration. We further identify that one key difficulty is that postprocessing often degrades the performance of the ensemble forecast during summer and early autumn. To mitigate, we additionally estimate on the training set whether postprocessing at a specific location is expected to improve the prediction. If not, the direct model output is used. This extension reduces the CRPS of the topographical model by up to another \(1.7 \%\) on average at the price of a slight degradation in calibration. In this case, the highest improvement is achieved for a lead time of 4 days.

来自集成系统的概率天气预报需要统计后处理，以产生校准的和清晰的预测分布。本文提出了一种集雨预测的面积覆盖后处理方法。我们依赖于集成模型输出统计（EMOS）方法，该方法生成具有参数分布的概率预测，其参数取决于集成预测的（统计）。一项针对瑞士各地每日降水量预测的案例研究突显出，观测地点的后处理确实提高了高分辨率总体预报，为\（4.5 \％\）提前期为1天时，平均CRPS降低。我们的主要目标是通过利用地形协变量来在不将模型绑定到测站的情况下实现这种改进。具体而言，回归系数是通过对训练数据的起点和预测位置之间的地形相似性进行加权来估计的。在我们的案例研究中，发现这种方法无需使用本地历史数据进行校准即可再现本地模型的性能。我们进一步确定，一个关键困难是后处理通常会降低夏季和秋季初的整体预报的性能。为了缓解这种情况，我们另外在训练集上估计在特定位置的后处理是否有望改善预测。如果不是，则使用直接模型输出。平均（\ 1.7 \％\），但校准略有下降。在这种情况下，可以在4天的交付时间内实现最高的改进。