  Machine learning (ML)-based parameterizations have been developed for Earth
System Models (ESMs) with the goal to better represent subgrid-scale processes
or to accelerate computations. ML-based parameterizations within hybrid ESMs
have successfully learned subgrid-scale processes from short high-resolution
simulations. However, most studies used a particular ML method to parameterize
the subgrid tendencies or fluxes originating from the compound effect of
various small-scale processes (e.g., radiation, convection, gravity waves) in
mostly idealized settings or from superparameterizations. Here, we use a
filtering technique to explicitly separate convection from these processes in
simulations with the Icosahedral Non-hydrostatic modelling framework (ICON) in
a realistic setting and benchmark various ML algorithms against each other
offline. We discover that an unablated U-Net, while showing the best offline
performance, learns reverse causal relations between convective precipitation
and subgrid fluxes. While we were able to connect the learned relations of the
U-Net to physical processes this was not possible for the non-deep
learning-based Gradient Boosted Trees. The ML algorithms are then coupled
online to the host ICON model. Our best online performing model, an ablated
U-Net excluding precipitating tracer species, indicates higher agreement for
simulated precipitation extremes and mean with the high-resolution simulation
compared to the traditional scheme. However, a smoothing bias is introduced
both in water vapor path and mean precipitation. Online, the ablated U-Net
significantly improves stability compared to the non-ablated U-Net and runs
stable for the full simulation period of 180 days. Our results hint to the
potential to significantly reduce systematic errors with hybrid ESMs.
