U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster

AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-theart (SOTA) models rely on specialized architectures and massive computational budgets, creating a high barrier to entry. We demonstrate that such complexity is unnecessary for frontier performance. We introduce U-Cast, a probabilistic forecaster built on a standard U-Net backbone trained with a simple recipe: deterministic pretraining on Mean Absolute Error followed by short probabilistic fine-tuning on the Continuous Ranked Probability Score (CRPS) using Monte Carlo Dropout for stochasticity. As a result, our model matches or exceeds the probabilistic skill of GenCast and IFS ENS at 1.5 ˝resolution while reducing training compute by over 10ˆcompared to leading CRPS-based models and inference latency by over 10ˆcompared to diffusion-based models. U-Cast trains in under 12 H200 GPUdays and generates a 15-day ensemble forecast in 3 seconds. These results suggest that scalable, general-purpose architectures paired with efficient training curricula can match complex domainspecific designs at a fraction of the cost, opening the training of frontier probabilistic weather models to the broader community. Our code is available at: https://github.com/Rose-STL-Lab/u-cast .