Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However, existing methods, such as quantile mapping, cannot effectively improve spatial patterns or temporal dynamics. We address this problem with a purely generative machine learning approach, combining unpaireddomain translation with a super-resolution foundation model. Our results show realistic spatial patterns and temporal dynamics, as well as reduced distributional biases in the processed ESM simulation.