Global change ecology demands predictive models that reconcile data‐driven learning with mechanistic theory to address complex, interconnected ecosystem challenges. Traditional process‐based approaches struggle with spatiotemporal parameterization, while purely data‐driven machine learning approaches suffer from extrapolation, interpretability, and physical consistency. Knowledge‐guided machine learning (KGML) bridges this div...