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Precipitation microphysics and the effective radiative forcing due to aerosol-cloud interactions (ERFaci) contribute to some of the largest uncertainties in general circulation models (GCMs) and are closely interrelated. This study shows that a sophisticated, two-moment prognostic precipitation scheme can simultaneously represent both warm rain characteristics consistent with satellite observations and a realistic ERFaci magnitude, thus reconciling compensating errors between precipitation microphysics and ERFaci that are common to many GCMs. The enhancement of accretion from prognostic precipitation and accretion-driven buffering mechanisms in scavenging processes are found to be responsible for mitigating the compensating errors. However, single-moment prognostic precipitation without the explicit prediction of raindrop size cannot capture observed warm rain characteristics. Results underscore the importance of using a two-moment representation of both clouds and precipitation to realistically simulate precipitation-driven buffering of the cloud response to aerosol perturbations.

Plain Language Summary Aerosol-cloud-precipitation interactions are among the most uncertain processes in general circulation models (GCMs). Many GCMs tend to represent cloud-to-rain conversion too frequently because of the use of simplified schemes for aerosolcloud interactions (ACI). It has been shown that model tuning cannot improve the simulated precipitation characteristics without unrealistically perturbing the energy budget through ACI. This indicates the presence of compensating errors in GCMs. These errors must be understood at a fundamental level and be mitigated in GCMs for reliable predictions of future climate change. This study shows that a sophisticated representation of precipitation, referred to as prognostic precipitation, can effectively mitigate these compensating errors in the model by incorporating precipitation-driven buffering mechanisms in the cloud response to aerosol perturbations. Results indicate that incorporating prognostic precipitation in GCMs leads to more reliable climate simulations.