Summary
Predicting detailed rainfall patterns from coarse weather data is hard, especially in places where models weren’t trained. This paper looks at a special AI method called wavelet diffusion models (WDMs) that can create detailed precipitation maps from smooth inputs. The authors test how well these models work in different US climate regions, including places they haven’t seen during training. They find that WDMs trained on one region can still do a good job elsewhere, and models trained on all regions work even better in spotting intense rain areas. This could help make weather predictions more detailed and accurate in many places, even with limited local data.
What this means in practice
- •For weather forecasting teams: Generate higher-resolution rainfall maps in regions lacking detailed local training data to improve forecast accuracy.
- •For hydrological modelers: Create spatially consistent and detailed precipitation inputs for runoff and flood risk models across diverse climatic regions.
Authors
Weikang Qian, Yixin Wen, Chugang Yi, Zhi Li, Lingcheng Li, Haizhao Yang
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.