PrecipNet: A transformer-based downscaling framework for improved precipitation prediction in San Diego County

Abstract

Study region

San Diego County (California, USA), with its complex topography and coastal climate variability, requires high-resolution precipitation data to support hydrological modeling and climate adaptation planning. However, the coarse spatial resolution of Global Climate Models (GCMs) limits their applicability in such a diverse and hydrologically sensitive region.

Study focus

This study introduces a two-stage hybrid statistical downscaling framework that combines Transformer-based deep learning with traditional machine learning for localized precipitation prediction. The goal is to downscale coarse-resolution CMIP5 precipitation data (2° × 2.5°, 3-h intervals) to a finer 10 km × 10 km grid appropriate for regional hydrological applications. The first stage employs HydroFusionNet, a Transformer-based classifier, to detect rainfall occurrence using spatial atmospheric predictors, thereby filtering out non-rain periods and improving computational efficiency. The second stage applies two regression models: a Random Forest with linear bias adjustment and PrecipNet, a Transformer-based model.

New hydrological insights for the region

PrecipNet achieved a Mean Absolute Error (MAE) of 1.24 mm, Root Mean Square Error (RMSE) of 1.62 mm, and R² of 0.94, outperforming the Random Forest baseline in accuracy and spatial generalization. HydroFusionNet demonstrated 92.75 % classification accuracy, enhancing rainfall detection. The framework reduces false positives, captures complex rainfall dynamics, and provides context-aware uncertainty estimation—offering a scalable, hydrologically meaningful tool for regional climate impact assessments and water resource decision-making in topographically complex areas like San Diego County.