Evaluating Cross-region Generalization for Wavelet-Diffusion Precipitation Downscaling

2026-09-23

AI Summary

This study addresses the limited generalization capability of diffusion models for precipitation downscaling in unseen geographic regions. Leveraging a wavelet-based diffusion model and MRMS radar data, we systematically evaluate cross-region and cross-event generalization performance against various training strategies and conventional interpolation methods. Results indicate that models trained on all regions achieve the best overall performance, though improvements are predominantly concentrated in high-reflectivity structures. Furthermore, this work innovatively reveals a strong correlation between spatial autocorrelation (Moran's I) and detection performance (CSI), with a sample-level correlation reaching 0.901. This finding demonstrates that single-region-trained models can maintain competitive performance across different regions, providing new empirical evidence for designing generalizable precipitation downscaling frameworks.

Abstract

Diffusion models have shown strong potential for kilometer-scale precipitation downscaling, but their performance in geographically unseen regions and event regimes remains insufficiently understood. Building on the wavelet diffusion model (WDM) framework, this study evaluates cross-region and cross-event generalization. Six 3 x 3 deg U.S. regions represent convective, winter, tropical, and atmospheric-river precipitation regimes. Low-resolution inputs are generated by block averaging NOAA Multi-Radar/Multi-Sensor (MRMS) composite reflectivity fields. A WDM trained only on Oklahoma (OK) samples and a WDM trained on all six regions are compared with nearest-neighbor and Bicubic interpolation. Model performance is evaluated using three metric families that measure image-domain reconstruction, spectral and distributional fidelity, and bin-wise precipitation detection. The OK-trained WDM remains competitive outside OK. Although the all-region WDM delivers the best and most consistent overall image-domain and detection performance, its gains are uneven across precipitation intensities. Bin-wise critical success index (CSI) over 5-dBZ reflectivity bins shows that WDM improvements concentrate in localized higher-reflectivity structures, which image-domain metrics partly obscure. In addition, the performance differences among samples are strongly associated with the spatial organization of the precipitation field, quantified by Moran's I as the spatial autocorrelation of each reflectivity bin. The sample-level Moran's I-CSI correlation stratified by sample intensity reaches 0.901 in all six regions, including regions unseen during training. Overall, these findings support future efforts to transfer downscaling models to regions with limited local training data and to generate globally consistent, high-resolution precipitation products.

Problem

• precipitation downscaling
• cross-region generalization
• diffusion models
• spatial autocorrelation
• reflectivity

Innovation

• Wavelet Diffusion Model
• Cross-region Generalization
• Precipitation Downscaling
• Spatial Autocorrelation
• Critical Success Index