JEPACast: Precipitation Nowcasting in a Decodable and Predictable Latent Space

Generative models have become a dominant approach to probabilistic radar-based precipitation nowcasting, yet the latent space where they generate has barely been examined. Most models rely on reconstruction-trained autoencoders, while advances focus on increasingly specialized generators without changing the representation. A simple decomposition of forecast error motivates two fundamental requirements for a forecasting latent space: *decodability* and *predictability*. We find that representations from a V-JEPA encoder trained on radar data remain strongly decodable while achieving higher predictability of future dynamics than widely used reconstruction-based representations. Building on this, we introduce **JEPACast**, which adapts the representation autoencoder paradigm to generative nowcasting by combining the V-JEPA encoder with a latent flow transformer and a ViT decoder. On SEVIR and MeteoNet, JEPACast outperforms various baselines on key forecast metrics, with larger gains for intense precipitation and longer lead times. These results highlight latent representation design as an important direction for generative precipitation nowcasting.