Mesoscale Convective Systems in Northeastern North America: identification and evaluation with the convection-permitting version of the Canadian Regional Climate Model.

Abstract

Extreme precipitation events are often associated with mesoscale meteorological phenomena, such as mesoscale convective systems (MCS). Convection-permitting models (CPMs), which operate at high spatial resolution, have enhanced our ability to represent atmospheric processes associated with mesoscale phenomena. This study aims to identify and evaluate MCSs and associated precipitation events in northeastern North America over the 2015-2022 period using various observational and model-based products. A tracking algorithm is used to identify and characterize MCSs in the ERA5 reanalysis, satellite-based (IMERG) data, radar observations (STAGE-IV and MRMS), and two simulations performed with the sixth version of the Canadian Regional Climate Model (CRCM6). These simulations are performed at horizontal grid spacings of 12 km and 2.5 km (CRCM6-12 and CRCM6-2.5, respectively), with the higher-resolution (2.5 km) simulation operating in CPM mode. Radar observations indicate that MCSs occur most frequently from May to September and typically initiate in the early afternoon. The lower horizontal resolution products (IMERG, CRCM6-12, and ERA5) underestimate both the mean occurrence and interannual variability of MCSs compared to the reference dataset STAGE IV-MERGIR, with mean biases of - 17%, - 50%, and - 88% and standard deviations of 23, 17, and 5.8 MCSs per year, respectively (reference standard deviation = 32 systems per year). The CRCM6-2.5 CPM model configuration accurately reproduces key MCS characteristics, including their intra-annual occurrence, size, duration, intensity, diurnal cycle, and their contribution to total and extreme precipitation. Notably, MCSs contribute more to extreme precipitation events than to the total precipitation. The CRCM6-2.5 model significantly improves the representation of convective processes at finer scales compared to lower-resolution products, although it slightly overestimates precipitation in comparison to radar observations.

Supplementary information: The online version contains supplementary material available at 10.1007/s00382-026-08102-6.