Post-Processing High-Resolution Ensemble Forecasts for Extreme Rainfall: Short-Term Skill Evaluation over Kyushu, Japan

Abstract. Extreme rainfall in Japan, exemplified by the August 2021 Kyushu event with multiple linear rainbands, continues to cause severe societal impacts, underscoring the need for reliable ensemble rainfall forecasts. This study evaluates how ensemble size, horizontal resolution, and integration period influence forecast skill using the SCALE-RM model. Four ensembles were examined: a coarser-resolution set (S1; 3.2 km, 100 members) and three finer-resolution sets (S2–S4; 800 m, 50 members), all initialized from ERA5 but with different setup and time integration. Mean Bias (MB) and Quantile Mapping (QM) corrections were applied, and skill was assessed using RMSE, probability maps, ETS, and BS. Before correction, none of the ensembles reproduced the moderate-to-heavy rainfall accumulation in northern Kyushu. S1 produced the lowest RMSE but failed to capture localized maxima, decayed rainfall too early, and missed the second peak on 12 August. After correction, performance diverged. S1 shows noticeable improvement, producing moderate to higher rainfall values in the northern region, though peak intensities remain slightly underestimated. S4 shows the strongest enhancement, successfully generating the extreme rainfall intensities in the rainband core and closely matching observations, indicating that its systematic biases were effectively removed. Overall, the findings demonstrate that high resolution alone does not guarantee improved skill; ensemble size and robust post-processing are equally critical. These insights inform both operational forecasting and controlled weather-modification experiments.