Original title: PepC-Global: A Basin-Tuned Probabilistic Tropical Cyclone Model with Enhanced Out-of-Sample Skill and Climate-Sensitive Over-Land Decay
Cong Gao, Ning Lin · September 2026
Predicting tropical cyclones worldwide is challenging because storms behave differently across separate ocean basins. Scientists developed PepC-Global, a global model tuned to specific ocean basins within one unified system. It treats cyclone life cycles as linked, chance-driven steps—storm birth, path, and strength—guided by large-scale climate conditions. Unlike older tools that assume storms weaken over land at fixed rates, this model weakens storms based on surrounding environmental factors. In tests on fresh data, its storm birth, path, and strength components all performed better than widely used linear models. Overall, PepC-Global successfully matches real-world storm counts, seasonal patterns, year-to-year shifts, tracks, coastline landfall rates, and maximum strengths, helping researchers assess future storm risks in changing climates.
You are baking a cake for friends in different countries. The same recipe fails because ovens and ingredients vary. You need a recipe that adjusts to local conditions. Here, the cyclone model uses a single framework but tunes itself to each ocean basin, and it makes the storm's decay over land depend on the local climate, like adjusting baking time for altitude.
This model improves how we predict tropical cyclone hazards, especially when storms hit land. By tuning to each ocean basin and making the decay over land sensitive to climate, it gives more accurate risk assessments for coastal communities. It also helps scientists study how cyclone activity might change in a warming world.
The abstract only compares PepC-Global to linear models, not to other advanced nonlinear or machine learning models. It does not discuss computational cost or data requirements. The evaluation is limited to historical observations and does not explicitly test future climate scenarios.
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PepC-Global: A Basin-Tuned Probabilistic Tropical Cyclone Model with Enhanced Out-of-Sample Skill and Climate-Sensitive Over-Land Decay — Cong Gao, Ning Lin
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.