Transferable Machine-Learning Practices for Earth-System Prediction and Diagnosis

Machine learning is increasingly used to analyze, predict, and interpret Earth-system behavior. Here we synthesize AI4OCEANS research to identify practical, transferable lessons for developing ML methods that remain robust when applied to real Earth-system data and are evaluated across regions, scales, and event types. We present methodological advances and common pitfalls encountered when building ML workflows for prediction and diagnosis across oceanic and atmospheric contexts. Emphasis is placed on (i) constructing physically meaningful predictors and representations that generalize beyond a single region or period, (ii) designing evaluation strategies that reflect scientific and decision-relevant objectives (including event- and regime-aware metrics where appropriate), and (iii) quantifying uncertainty and interpretability in ways that support scientific insight rather than purely empirical skill. We further discuss when hybrid strategies—combining statistical learning with physical constraints or dynamical context—improve robustness in specific applications. By framing diverse studies through shared methodological questions across geophysical systems (from coastal ocean change through high-impact atmospheric events and into bycatch threats to marine wildlife), the produced frameworks provide guidance for ML development that is directly relevant to Earth-system modelling and prediction, particularly for variability, extremes, and environmental risks and impacts under anthropogenic influences.