Mesoscale convective systems (MCSs) are the main source of precipitation in the tropics and parts of the mid-latitudes and are responsible for high-impact weather worldwide. Studies showed that deficiencies in simulating mid-latitude MCSs in state-of-the-art climate models can be alleviated by kilometer-scale models. However, whether these models can also improve tropical MCSs and weather we can find model settings that perform well in both regions is understudied. We take advantage of high-quality MCS observations collected over the Atmospheric Radiation Measurement (ARM) facilities in the U.S. Southern Great Plains (SGP) and the Amazon basin near Manaus (MAO) to evaluate a perturbed physics ensemble of simulated MCSs with 4\,km horizontal grid spacing. A new model evaluation method is developed that enables to distinguish biases stemming from spatiotemporal displacements of MCSs from biases in their reflectivity and cloud shield. Amazon MCSs are similarly well simulated across these evaluation metrics than SGP MCSs despite the challenges anticipated from weaker large-scale forcing in the tropics. Generally, SGP MCSs are more sensitive to the choice of model microphysics, while Amazon cases are more sensitive to the planetary boundary layer (PBL) scheme. Although our tested model physics combinations had strengths and weaknesses, combinations that performed well for SGP simulations result in worse results in the Amazon basin and vice versa. However, we identified model settings that perform well at both locations, which include the Thompson and Morrison microphysics coupled with the Yonsei University (YSU) PBL scheme and the Thompson scheme coupled with the Mellorâ\euro“Yamadaâ\euro“Janjic (MYJ) PBL scheme.