Abstract Forecasting Severe Convective Wind (SCW) events remains challenging due to unresolved precursors. Using 8‐year (2016–2023) high‐resolution soundings from China radiosonde network, we establish observational pre‐SCW thresholds, revealing divergent pre‐storm pathways that advance beyond conventional SCW paradigms: Wet SCW events exhibit abrupt energy cycling (≤20‐min Convective Available Potential Energy collapse coinciding with about 15% moisture surges) coupled with mid‐level cyclonic rotation breakdown, while dry SCW events show a distinct two‐stage kinetic energy descent, featuring initial downward wind kinetic energy transfer from 5 to 2 km altitude within −40 to −20 min, followed by rapid surface downdraft acceleration. Physically, wet events derive intensity from deep instability amplified by moisture enhancement, driving robust convection. Dry events originate from shallow instability released through pulsed downdrafts with weaker gusts. Machine learning attribution (SHAP >0.24) establishes precipitable water as the dominant discriminator (wet: >48 mm; dry: <40 mm). These pre‐storm signatures have great implications for nowcasting SCW events.

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