“…Past studies have identified the importance of environmental parameters and how they can aid in the prediction of tornadic supercells (Davies‐Jones, 1984; Klemp, 1987; Markowski et al., 2002; Rotunno, 1981), and since then many studies have focused on the evolution, the spatial distributions, and the temporal distributions of those environmental parameters to characterize regional, diurnal and seasonal differences in tornado‐favorable environments (R. L. Thompson et al., 2012; R. Thompson et al., 2013; A. Anderson‐Frey et al., 2016; Reames, 2017). In recent years, with the rise in popularity of machine learning and deep learning, there is a substantial body of work that uses environmental parameters derived from near‐storm proximity soundings to classify the shared characteristics of tornadic storms that occur in different regions, seasons, and times of day (Lu et al., 2015; A. K. Anderson‐Frey et al., 2017; Warren et al., 2021), in addition to providing probabilistic prediction of tornadoes (Coffer et al., 2020; Shield & Houston, 2022). Most studies assume that tornadic environments are substantially different from the typical environments for a specific location at a certain time of year and time of day in which tornadic weather does not occur (hereafter referred to as baseline environments), but studies rarely quantify the differences between the two.…”