Abstract Geodetic observations along convergent margins have achieved unprecedented resolution in detailing deformation associated with earthquake cycles. A comprehensive understanding of how to best interpret these data for forecasting remains crucial. Here, we combined analogue seismo‐tectonic models of a megathrust and Explainable Artificial Intelligence (XAI) to characterize the link between earthquakes and deformation. We utilized deformation features to train convolutional neural networks (CNN) that forecast the time left before a laboratory earthquake. We then used Integrated Gradients (IG), an XAI technique, to identify areas and features contributing to model forecasts. CNNs perform better compared to decision trees utilizing sparse point‐wise features highlighting the importance of spatial patterns. IG reveals the significance of trench‐perpendicular deformation downdip the rupturing asperity, trench‐parallel deformation inland, and local deformation curl, in forecasting rupture timing. These emphasize the need for dense networks to monitor deformation and suggest patterns that may signify rupture imminence in convergent margins.

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