The peak hurricane season in August and September demands immediate monitoring and response planning to minimize environmental destruction and protect communities. The protection of communities from powerful storms requires ongoing data analysis and improved infrastructure and public education programs for building resilience. The prediction of hurricane behavior faces significant challenges because of its complex data patterns. The complex behavior of hurricane formation and movement and intensity cannot be predicted through basic linear regression or exponential smoothing methods. Accordingly, this research used multiple statistical runs to study hurricane patterns in the United States.

The research applied Extreme Value Theory (EVT) to measure rare and powerful hurricane probabilities through distribution tail analysis. The maximum observed values from EVT calculations enabled researchers to establish return periods for extreme hurricanes. The research showed that hurricanes with lower intensity occur more frequently because their return periods are shorter, and the intense hurricanes become less frequent as storm power increases in power but still present a possibility for occurrence.

Our observation demonstrates that predictive modeling plays a vital role in understanding hurricane patterns and precipitation changes which leads to enhanced readiness for upcoming severe weather events.

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