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As the spatial resolution of general circulation models (GCMs) increases and storms and clouds can be resolved, the underlying cloud microphysics still need to be parameterised. This is known to be a major source of uncertainty in climate and weather simulations. The established parameterisations use bulk moment schemes, where the conversion of cloud and rain droplets is approximated through empirical relationships. Particle-based superdroplet simulations would provide a more accurate representation but are typically not feasible for use in GCMs.We couple SuperdropNet, an ML emulator for warm rain cloud microphysics trained on superdroplet simulations, to ICON. Previously, we validated the coupled model in an idealised cloud microphysics test case and showed that SuperdropNet runs stable and provides reasonable precipitation patterns.Now we move towards a climate model experiment with 10 km horizontal resolution in an AMIP setup to investigate SuperdropNet’s feasibility and interaction with ICON in a realistic setting. Coupling SuperdropNet to ICON is achieved using FTorch. We are able to run ICON on 128 nodes on the CPU partition of the HPC system Levante with minimal overhead. Conditions beyond the training data range of SuperdropNet lead to negative feedback loops and impact the long-term stability of the coupled simulation. Therefore, we implement physics-based constraints that improve stability. Initial results show mean surface precipitation is very similar to using the bulk scheme approach. SuperdropNet simulates a faster cloud-to-rain transition which impacts cloud water mass and rain droplet size. This has consequences for the radiation budget and the frequency distribution of precipitation. Furthermore, we show that an autoregressive rollout of SuperdropNet that allows for longer GCM time steps runs stable and does not impact results. Finally, we test SuperdropNET’s generalisation capabilities in a 4K warmer world.