Stress-Testing Dynamical and Generative Downscaling Using Subseasonal Extreme Precipitation Forecasts

2026-09-10

AI Summary

研究通过比较动力模型WRF与生成模型在不同降水极端事件中的表现，解决了次季节预报中因空间分辨率低导致的极端降水预测问题。

Abstract

Coarse spatial resolution limits the ability of subseasonal prediction models to resolve extreme precipitation. Downscaling with either dynamical or deep generative models can overcome this issue, but the comparative performance of these models for extremes across different atmospheric regimes remains poorly understood. In this work, we evaluate the Weather Research and Forecasting (WRF) model against a diffusion-based generative model by downscaling two physically distinct, extreme precipitation events up to lead times of 3 weeks. For a fair comparison with WRF, which can downscale boundary conditions from different driving models without model-specific training, the diffusion model is trained in an unpaired fashion. Both approaches improve upon the raw European Centre for Medium-Range Weather Forecasts forecasts, in comparison to fused rain gauge-radar observations in Switzerland (CombiPrecip), but exhibit regime-dependent strengths. WRF achieves the highest probabilistic skill for a multicell, non-stationary event. Conversely, the diffusion model is more consistent across different performance metrics for the two events, outperforming WRF in a more stationary supercell event. These results demonstrate that explicit dynamical modeling can add value for specific precipitation events for subseasonal lead times, and that generative downscaling adds value more broadly in different situations.

Problem

• subseasonal extreme precipitation
• downscaling
• dynamical models
• generative models
• atmospheric regimes

Innovation

• dynamical downscaling
• generative models
• extreme precipitation
• subseasonal forecasts
• regime-dependent performance