Resume/ Abstract
Authors: Milena Alpizar1,2 , Alejandro Di Luca 1,2 and Philippe Gachon 2,3
1Département des sciences de la Terre et de l’atmosphère , Université du Québec à Montréal (UQAM)
2Centre pour l’étude et la simulation du climat à l’échelle régionale (ESCER)
3Département de géographie, UQAM
Extreme precipitation events are often related to mesoscale weather systems such as mesoscale convective systems (MCSs), which are large clusters of thunderstorms. Using very high-resolution models—called convective-permitting models (CPMs)—makes it possible to simulate these storms more accurately. This study focuses on MCSs and the heavy rainfall they produced in northeastern North America between 2015 and 2022. We used various data sources—including radar, satellite, reanalysis, and model simulations—to identify and track MCSs with a detection and tracking algorithm. We compared results from two versions of the Canadian Regional Climate Model (CRCM6): one with a 12 km resolution, and a higher-resolution version (2.5 km) that operates in CPM mode. Radar observations show that MCSs happen most often between May and September and usually begin in the early afternoon. Datasets with lower spatial resolution—like IMERG, CRCM6-12, and ERA5—miss many of these systems, detecting 18%, 51%, and 88% fewer MCSs, respectively, compared to radar data. The high-resolution CRCM6-2.5 model provides a more accurate representation of MCS behavior. It successfully reproduces their seasonal frequency, spatial extent, duration, and typical timing of initiation. Notably, MCSs contribute more significantly to extreme precipitation events than to total rainfall. Although CRCM6-2.5 slightly overestimates rainfall compared to radar data, it adds value over lower-resolution models by more accurately representing convective precipitation and extremes.