  Modern climate projections often suffer from inadequate spatial and temporal
resolution due to computational limitations, resulting in inaccurate
representations of sub-grid processes. A promising technique to address this is
the Multiscale Modeling Framework (MMF), which embeds a kilometer-resolution
cloud-resolving model within each atmospheric column of a host climate model to
replace traditional convection and cloud parameterizations. Machine learning
(ML) offers a unique opportunity to make MMF more accessible by emulating the
embedded cloud-resolving model and reducing its substantial computational cost.
Although many studies have demonstrated proof-of-concept success of achieving
stable hybrid simulations, it remains a challenge to achieve near
operational-level success with real geography and comprehensive variable
emulation that includes, for example, explicit cloud condensate coupling. In
this study, we present a stable hybrid model capable of integrating for at
least 5 years with near operational-level complexity, including real geography,
seasonality, explicit cloud condensate predictions, and land coupling. Our
model demonstrates skillful online performance in metrics such as 5-year zonal
mean biases compared to previous MMF emulation studies. The monthly error
against reference MMF simulations with the same initial condition approaches
the fundamental predictability limit. Key factors contributing to our online
performance include an expressive U-Net architecture, additional input features
that include large-scale forcings and convection memory, and physical
thermodynamic constraints for microphysics. With microphysical constraints
mitigating unrealistic cloud formation, our work is the first to demonstrate
realistic multi-year cloud condensate climatology under the MMF framework. Our
work showcases ML parameterization's potential for operational-level climate
simulations.
