The recent revolution in data-driven methods for weather forecasting has lead to a fragmented landscape of complex, bespoke architectures and training strategies, obscuring the fundamental drivers of forecast accuracy. Here, we demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized training heuristics. We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector that resolves high-resolution physics.

Accurate short-term prediction of clouds and precipitation is critical for severe weather warnings,
aviation safety, and renewable energy operations. Forecasts at this timescale are provided by
numerical weather models and extrapolation methods, both of which have limitations. Mesoscale
numerical weather prediction models provide skillful forecasts at these scales but require significant
modeling expertise and computational infrastructure, which limits their accessibility.
Extrapolation-based methods are computationally lightweight but degrade rapidly beyond 1-2