Deep Learning Emulation of Convective Instability and Near-Surface Fields from ERA5

As climate variability intensifies, extreme weather events are expected to change its frequency and severity, increasing the need for high-resolution meteorological data capable of resolving small-scale processes such as convective storms, urban heat islands, and extreme wind events. The ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) is widely used for global and regional analyses, but its coarse spatial resolution limits its applicability for fine-scale impact studies. Dynamical downscaling using physical models can bridge this gap but this approach remains computationally expensive. As an alternative, machine learning based models that learn to map coarse data into data produced by physical models offer a computationally inexpensive solution.

Here, we present a multivariate deep learning framework based on a UNet architecture to emulate and downscale key near-surface and convective variables from ERA5 to convection-permitting resolution using limited data. Five low-resolution atmospheric predictors at three pressure levels (850, 700 and 500 hPa), together with five single level variables and a high-resolution elevation map is used as input for the model, which aims to emulate Most Unstable Convective Available Potential Energy (MUCAPE) and downscale 2m temperature and 10m wind components. The model is trained using ERA5 data at 25 km resolution as input and CONUS404, a WRF-based regional hydroclimate reanalysis at 4 km resolution over the contiguous United States, as the target.

Relative to ERA5, the downscaled fields exhibit substantial error reductions, with root-mean-square error improvements of 35.7% for MUCAPE, 20.0% for 2 m temperature, 23.0% for zonal wind, and 20.8% for meridional wind. The model reproduces fine-scale spatial structure, realistic value distributions, and seasonal and temporal variability, and demonstrates skill in representing extreme convective environments, including those associated with hurricanes.

These results highlight the ability of multivariate deep learning to capture complex inter-variable relationships in the atmosphere. In particular, deep learning–based MUCAPE emulation provides a computationally efficient alternative to traditional diagnostic calculations, enabling spatially detailed and readily accessible datasets for severe weather analysis and climate impact studies using a limited set of input variables.