Associations Between Storm Exposure Patterns and Metabolic Syndrome Risk in Chinese Adults: A CHARLS-Based Prospective Cohort Study

Background: Rising metabolic syndrome (MetS) prevalence in China coincides with increased frequency and intensity of urban rainstorms under climate change. While acute impacts of extreme rainfall are documented, evidence on long-term associations with chronic metabolic conditions remains limited. Methods: This prospective cohort study analyzed 16,278 middle-aged and older adults from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2020). Five rainstorm exposure indicators (frequency, duration, intensity, peak rainfall, total volume) were assessed. Cox regression models evaluated associations with MetS incidence, adjusted for sociodemographic, behavioral, and clinical confounders. Spatial analyses included:(1)Global/Local Moran’s I to detect spatial clustering of provincial MetS prevalence.(2)Geographically Weighted Regression (GWR) to quantify location-specific associations between rainstorm exposures and MetS. Results: (1)Spatial Clustering:Significant spatial autocorrelation in MetS prevalence was observed (Global Moran’s I= 0.288, z-score = 4.025, p< 0.001), identifying high-high clusters (hotspots)​in Northern China and low-low clusters (coldspots)in Southern China. (2)Rainstorm-MetS Associations:Rainstorm Frequency:Nationwide negative association with MetS risk (HR = 0.94, 95% CI: 0.93–0.95), strongest in coastal regions (GWR coefficients: −0.027 to −0.017).Rainstorm Duration:Positive association (HR = 1.03, 95% CI: 1.02–1.04), with pronounced effects in Central/Eastern provinces(e.g.,Henan,Shandong;GWR coefficients: up to+0.205).Peak Rainfall:Spatially heterogeneous—protective in the South (GWR: −0.175 to −0.195) but detrimental in the Northwest (GWR: +0.193 to +0.205).Dose-Response:Non-linear patterns (U/J-shaped) emerged, with extreme exposures attenuating protective effects. Conclusion: Rainstorm exposures exhibit dual protective-risk effects on MetS, moderated by spatial context. Frequency and moderate peak rainfall reduce risk, while prolonged duration and extreme intensity elevate it. Spatial analyses reveal distinct geographic vulnerability patterns, underscoring the need for region-specific public health interventions targeting climate-resilient metabolic health strategies.