Heavy precipitation episodes are particularly hazardous in mid-sized alpine catchments where runoff tends to increase rapidly after strong rainfall, leaving limited time for warning. A high-quality, unbiased forecast of heavy precipitation with long enough lead times (2–5 days) and an adequate spatial resolution is thus crucial for decision makers. However, at present, weather forecast models are sometimes still too coarsely resolved for such catchments or limited to short lead times. Here we present a computationally cheap post-processing approach for operational applications to bias-correct deterministic heavy precipitation forecasts at medium-range lead times. We assessed forecast performance of uncorrected ECMWF Integrated Forecasting System (IFS) high-resolution (HRES) and ensemble median run (ENS) forecasts with lead times of 2 and 5 days between 2010 and 2019 and evaluated the improvement after interpolation and bias correction. The study is based on observations from 787 meteorological stations, with a focus on areal precipitation in three medium-sized Swiss catchments (Emme, Simme, and Vispa). We found a significantly higher performance of the HRES forecast compared to ENS, especially for a lead time of 5 days. The post-processing approach that we present in this study removes large biases, and lowers false alarm rates. Nonetheless, improvements may be limited for single heavy precipitation events where the model considerably underestimates daily precipitation. Also, a lower false alarm rate can be at the cost of lower hit rates. Quantile mapping is clearly limited for short term prediction in the cases where the error does not substantially exceed natural variability. Despite these limitations, we conclude that bias-corrected forecasts can help improving forecast credibility, which is a key element for decision makers to take actions.

在中等规模的高山集水区，强降水事件尤其危险，因为强降雨后径流往往会迅速增加，从而导致预警时间有限。因此，具有足够长的提前期（2-5 天）和足够的空间分辨率的高质量、无偏见的强降水预报对于决策者来说至关重要。但是，目前，对于此类流域，天气预报模型有时仍然过于粗糙，或者仅限于较短的交付时间。在这里，我们提出了一种计算成本低廉的后处理方法，用于操作应用程序，以在中期提前期对确定性强降水预报进行偏差校正。我们评估了未校正的 ECMWF 综合预报系统 (IFS) 高分辨率 (HRES) 和集合中值运行 (ENS) 预测的预测性能，提前期为 2010 至 2019 年的 2 天和 5 天，并评估了插值和偏差校正后的改进。该研究基于 787 个气象站的观测结果，重点关注三个中等规模的瑞士流域（Emme、Simme 和 Vispa）的区域降水。我们发现 HRES 预测的性能明显高于 ENS，尤其是在 5 天的提前期。我们在本研究中提出的后处理方法消除了较大的偏差，并降低了误报率。尽管如此，对于模型大大低估每日降水量的单一强降水事件，改进可能有限。还，较低的误报率可能以较低的命中率为代价。在误差基本上不超过自然变异性的情况下，分位数映射显然受限于短期预测。尽管有这些限制，我们得出结论，偏差校正预测有助于提高预测可信度，这是决策者采取行动的关键因素。