Explainable AI (XAI) emerged to clarify the decision-making of complex deep learning models, but standard XAI methods are often uninformative on Earth system models due to their high-dimensional and physically constrained nature. We introduce “physical XAI,” which adapts XAI techniques to maintain physical realism and handle autocorrelated data effectively. Our approach includes physically consistent perturbations, analysis of uncertainty, and the use of variance-based global sensitivity tools. Furthermore, we expand the definition of “physical XAI” to include meaningful interactive data analysis. We demonstrate these methods on two Earth system models: a data-driven global weather model and a winter precipitation type model to show how we can gain more physically meaningful insights.