Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ?200 km version of NOAA’s FV3GFS) evolve more like a fine-resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi-year ML-corrected simulations. Here four fine-resolution (25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by -4, 0, +4, and +8 K. A data set of state-dependent corrective tendencies is then derived through nudging the ?200 km model to the coarsened state of the fine-resolution simulations in each climate. Along with the surface radiative fluxes, the corrective tendencies of temperature and specific humidity are machine-learned as functions of the column state. ML predictions for the fluxes and corrective tendencies are applied in 5.25 years ?200 km resolution simulations in each climate, and improve the spatial pattern errors of land precipitation by 8%-28% and land surface temperature by 19%?25% across the four climates. The ML has a neutral impact on the pattern error of oceanic precipitation.