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This research aims to create a climatology of mesoscale convective system hazards and assess numerical model representation.

A Climatology of Mesoscale Convective System Hazards in the United States and Their Representation in a Convection‐Permitting Model

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This research aims to create a climatology of mesoscale convective system hazards and assess numerical model representation.
Constructed a climatology using a 13-year MCS data set and storm reports.
Extracted MCS-related and environmental variables for machine learning model training.
Developed three models to predict flash floods, severe, and significant-severe events.
Flash flood and severe models accurately distinguish hazard-producing MCSs from non-producing ones.
The significant-severe model showed limited skill due to sample size constraints.
Applied models to the full MCS archive to create a detailed warm season hazard climatology.

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Observational study establishes MCS hazard climatology, predicting severe events using machine learning.

Cui et al. (2026) studied this question.

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