A satellite-based climatology is presented of 9607 mesoscale convective systems (MCSs) that occurred over the central and southeastern United States from 1996-2017. This climatology is constructed with a fully automated algorithm based on their cold cloud shields, as observed from infrared images taken by GOES-East satellites. The geographical, seasonal, and diurnal patterns of MCS frequency are evaluated, as are the frequency distributions and seasonal variability of duration and maximum size. MCS duration and maximum size are found to be strongly correlated, with coefficients greater than 0.7. Although previous literature has sub-classified MCSs based on size and duration, we find no obvious threshold that cleanly categorizes MCSs. The Plains and Deep South are identified as two regional modes of maximum MCS frequency, accounting for 21% and 18% of MCSs, respectively, and these are found to differ in the direction and speed of the MCSs (means of 16 ms−1 and 13 ms−1), their distributions of duration and size (means of 12.2 h, 176000 km2 and 9.6 h, 108000 km2), their initial growth rates (means of 7.6 km2 s−1 and 6.1 km2 s−1) and many aspects of the seasonal cycle. The lifetime patterns of MCS movement and growth are evaluated for the full domain and for the two regional modes. The growth patterns and strong correlation between size and duration allow for a parabolic function to represent the MCS lifecycle quite well in summary statistics. We show that this satellite-based climatology supports previous studies identifying favorable environments for mesoscale convective systems.
