DescriptionThe transformative power of ML unleashed in Earth System modelling is taking shape. Recent advances in building hybrid models combining mechanistic Earth system models grounded in physical understanding and machine learning models trained from huge amounts of data show promising results. Full replacement models such as FourCastNet have also made continuous progress and shown impressive performance. Hybrid models that include, for instance, ML based parameterizations, can provide a substantial speed-up or qualitative improvement if trained on high-resolution data compared to parameterizations based on few data points. However, the ongoing implementation of such models poses technical challenges, while the acceptance of hybrid approaches and full replacement models critically depends on the means scientists have for validating them. This minisymposium will discuss the technical challenges in building hybrid models, such as the coupling of Earth system and machine learning model components via embedded Python or MPI, and report on accomplished milestones and benchmark results. For further adoption of ML methods, it is also crucial to establish the credibility of ML-enhanced Earth system models through statistical reproducibility. The symposium will discuss arising questions around the validation of hybrid approaches, including metrics for training and evaluation that emphasize spatial features.