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Abstract Convective Self‐Aggregation (CSA) is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative‐Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid‐scale processes. This study examines the role of mixing and entrainment, provided by the turbulence model and the implicit numerical diffusion. The study compares the results of two models, WRF and SAM, by varying turbulence models, initial conditions, and horizontal spatial resolution. At a coarse grid resolution of 3 km, the removal of turbulent mixing prevents CSA in models with low numerical diffusivity but is preserved in models with high numerical diffusivity. When the horizontal grid resolution is refined to 1 km, CSA can only be achieved by increasing explicit turbulent mixing, even with a small amount of shallow clouds. Therefore, the sensitivity of CSA to horizontal grid resolution is not primarily caused by the decrease in shallow clouds. The analysis of the total water path spectrum suggests that the amplitude of initial humidity perturbations introduced by convection in the free troposphere is the key factor. This amplitude is regulated by turbulent mixing and diffusion at small scales. Prior to the onset of CSA, increased mixing makes updrafts more sensitive to the dryness of the free troposphere, which strengthens the moisture‐convection feedback. This leads to an increased distance between convective cores and a stronger humidity perturbation in the free troposphere, which can destabilize the RCE state.

Abstract The radiative‐convective equilibrium (RCE) of two models exhibiting convective aggregation has been compared. The goal of the work, following the suggestion from the RCE Model Intercomparison Project (RCEMIP), is to identify key parameters controlling self‐aggregation in RCE for both models, to discuss the processes controlled by these parameters and to underline the models similarities and differences. The two cloud resolving models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), present similar statistics concerning precipitation, but different warming, and drying of the atmosphere, within the spread of the RCEMIP values. On the other hand, the two models show different strengths of the moisture feedback, due to the different saturation of the sub‐cloud layer. A saturated sub‐cloud layer in ARPS (which was not artificially imposed in the numerical setup) allows the localization of convection in moist regions, by weakening the negative influence of cold pools. Such a mechanism leads to a lower degree of aggregation (based on three organization metrics) and a weaker effect of the organized state on the average domain statistics in ARPS. Stronger cold pools in SAM, instead, help the creation of shallow clouds in dry regions, increasing the longwave feedback responsible for their expansion; while delocalizing convection in moist regions and therefore opposing high‐cloud radiative‐feedback. Further experiments are needed to generalize such findings to other RCEMIP models, also investigating the role of microphysics and turbulence schemes in regulating such mechanisms.