Improving Extreme Precipitation Forecasts via Cascade Binary Classification With Diffusion Post‐Processing

Abstract

Accurate forecasting of extreme precipitation remains a critical challenge, owing to its heavy‐tailed distribution and severe class imbalance that limit conventional deep learning approaches. This study presents a two‐stage cascade‐diffusion framework for extreme precipitation forecasting over eastern China (110°–130°E, 20°–40°N). The first stage employs a cascade binary classification module comprising 15 independent U‐Net models. Each model was trained for a specific precipitation threshold and integrated via hierarchical stacking, producing a coarse‐resolution (0.25°) diagnostic field with reliable spatial localization across intensity levels. The second stage employs a diffusion‐based post‐processing module for simultaneous bias correction and probabilistic spatial downscaling from 0.25° to 0.1°. This step recovers distributional continuity and preserves the heavy‐precipitation tail via higher‐percentile ensemble aggregation (P80, P90). The framework uses ERA5 reanalysis and GPM IMERG over 2013–2023, with 2023 as an independent test year. Results demonstrate that the cascade module achieves Critical Success Index (CSI) improvements of 25%–54% over regression and multi‐task learning baselines at the 50–100 mm 6h ⁻¹ thresholds. The diffusion post‐processing module further improves the distributional fidelity, with P80 and P90 aggregation both achieving strong detection skill at high thresholds. While P90 attains the highest CSI at extreme thresholds, P80 provides a more balanced performance with better‐controlled frequency bias. Variable importance analysis via sequential feature selection reveals physically interpretable threshold‐dependent pressure‐level contributions. Near‐surface levels dominate across all intensities, while upper‐tropospheric levels provide progressively greater marginal skill for extreme events. These results establish the cascade‐diffusion framework as a practically viable approach for operational extreme precipitation forecasting.