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

Machine-learning (ML) weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initial conditions, tying them to expensive infrastructure and limiting the practical speed and accuracy gains of ML. More recently, ML-based DA systems have been proposed, which are often trained and evaluated end-to-end with a forecast model, making it difficult to assess the quality of their analysis fields.