Earth-JEPA：Toward efficient and transferable Earth-system latent representation learning

Deep learning for Earth system forecasting has advanced rapidly, mostly through end-to-end supervision on reanalysis data for individual tasks. This training paradigm tends to be less efficient on spatiotemporally smooth fields, while making cross-task adaptation relatively expensive. To overcome these challenges, we introduce Earth-JEPA (E-JEPA), a reconstruction-free, multi-resolution, modality-wise Joint-Embedding Predictive Architecture for efficient and transferable Earth-system prediction. Modality-specific branches encode satellite observations, atmospheric reanalysis and geographic fields into a multi-resolution latent state. A latent-pyramid predictor with Scale-Specific Attention forecasts future representations, each supervised by its corresponding exponential moving average (EMA) target through a State-Transition Loss. On WeatherBench2, Earth-JEPA achieves 17.8% higher 24-hour U850 anomaly correlation coefficient (ACC) than a leading large-scale supervised weather forecaster using only about 12.8% of their estimated training compute. Experiments on SEVIR and our proposed EarthCastBench benchmark demonstrate transferability across time-intervals and sensors, robustness to missing reanalysis, and strong performance on dense tasks, e.g., precipitation nowcasting, and forecasting of surface and atmospheric variables. These results suggest a promising direction for efficient and transferable Earth-system latent representation learning. Code and data will be released upon publication.