Abstract
Severe convective storms (SCSs; tornadoes, large hail, and damaging convective wind gusts) can produce socioeconomic impacts ranging from billion-dollar hailstorms to deadly tornado outbreaks. Weather-sensitive economic sectors (e.g., agriculture, aviation, insurance, logistics) impacted by these mesoscale extreme events have evolved to anticipate and, in some cases, mitigate their impact; however, these expectations may be disrupted by shifts in the climatological frequency, intensity, and efficiency of these storm-scale extremes in a future climate. In addition to a changing climate, it is not yet well understood why some years tally record high SCS reports, whereas others exhibit record low counts. Ultimately, understanding how climate regimes may modify these relatively low probability—but high impact—events could help reduce this uncertainty, thus empowering stakeholders to anticipate change, mitigate impacts, and incorporate resiliency strategies. More research is needed, particularly using novel computational methods such as dynamical downscaling and machine learning to understand the full impact that climate variability and change may have on these extreme weather events.
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