Global kilometer-scale models are the future of Earth system models as they can explicitly simulate organized convective storms and their associated extreme weather. Here, we comprehensively examined tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non-hydrostatic domains) models for both summer and winter phases by applying eight different feature trackers to the simulations and satellite observations. Although different trackers produce substantial differences (a factor of 2-3) in observed MCS frequency and their contribution to total precipitation, model-observation differences in MCS statistics are more consistent among the trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi-model mean biases of 2.9% over land and -0.5% over ocean. However, most models underestimate the MCS precipitation amount (23%) and their contribution to total precipitation (17%) relative to observations. These biases show large inter-model variability, but are generally smaller over land (13%) than over ocean (21%) on average. MCS diurnal cycle and cloud shield characteristics are better simulated than precipitation. Most models overestimate MCS precipitation intensity and underestimate stratiform rain contribution (up to a factor of 2), particularly over land. Models also predict a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, and many models simulate a greater sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed in this work provide process-oriented diagnostics for future model development efforts.

Mesoscale Convective Systems (MCSs) play a critical role in tropical rainfall patterns and circulations. To reduce persistent biases and improve understanding of the climate system, international groups have called for unprecedented investment in global convection-permitting (CP) climate models. It is essential such models accurately represent MCSs, and in particular environmental interactions such as dynamical control by wind shear. We show that in representative current generation CP simulations, MCS updraft entrainment decreases with shear, leading to a realistic increase of extreme rainfall. We find the control of environmental shear extends to mean storm rainfall and anvil heights. The simulation of these effects depends strongly on model physics in both CP and parameterised models. We show that in West Africa, MCS shear response influences the zonal distribution of storm diabatic heating, modifying upscale impacts of convection. Our results demonstrate key tests for focused process-based assessment of CP model fidelity.