Mesoscale Convective Systems (MCSs) are drivers of high-impact severe weather events, often resulting in profound economic and social impacts. Despite their significance, NWP models frequently struggle to accurately reproduce precipitation and low-level conditions associated with these systems. This talk evaluates the performance of MPAS in reproducing MCSs and associated severe weather across two distinct cases: the PERiLS field campaign IOP2 in the Southeastern U.S. (30–31 March 2022) and a severe convective event in Southern Brazil (7–8 November 2025). A series of sensitivity experiments were conducted to systematically isolate the impacts of initialization lead time, physics suites, and modeling frameworks (60–3 km variable-resolution global versus regional 3 km). For the U.S. case, object-based MCS tracking revealed that the 60–3 km simulation better reproduced the observed storm than the regional 3 km simulation. Surface verification revealed that the 60–3 km simulation was skillful in reproducing precipitation. Furthermore, physics sensitivities within the global framework showed that default physics configurations (CP and MR) yielded marginal changes in precipitation placement but generate more intense radar reflectivity cores compared to the baseline NOAA-GSL suite. Finally, simulations of the Southern Brazil case demonstrate MPAS-A's robust capability to capture MCS features. In addition, results from Amazon MCS cases further highlight the persistent challenges of reproducing convective organization and precipitation evolution in tropical environments. These findings offer citical insights into MPAS capabilities for high-impact convective applications.
