A four‐dimensional nonlinear dynamical model for an integrated early warning system of rapid‐onset meteorological hazards

Abstract Rapid‐onset meteorological hazards (ROMHs) — such as flash droughts, convective windstorms, and cold surges — pose critical challenges to forecasting due to their abrupt onset, nonlinear behavior, and cascading societal impacts. This study introduces the Early Warning System Integrated (EWSI) model, a four‐dimensional nonlinear dynamical framework designed to anticipate ROMHs through emergent system behavior. EWSI integrates four interacting state variables — Weather Sensitivity (WS), Recovery Capacity (RC), Threat Propagation Potential (TPP), and Behavioral Tendency Index (BTI) — each governed by coupled differential equations driven by a composite atmospheric forcing derived from 11 reanalysis‐based predictors. The model simulates hallmark features of complex systems including critical slowing down, hysteresis, and bifurcation, generating a continuous, interpretable risk index for early‐warning applications. Using synthetic forcing scenarios, EWSI successfully anticipates heatwaves, convective storms, and compound cold‐wave events, with lead times ranging from three to 60 hours. The model produces sharp, calibrated forecasts (Brier Score ), and negative Lyapunov exponents confirm its internal dynamical stability. Sensitivity analysis identifies RC‐related parameters as primary drivers of risk amplification, underscoring the importance of resilience dynamics. EWSI provides a computationally efficient, modular, and interpretable platform that links nonlinear dynamics to operational meteorology. It offers a pathway to integrate early‐warning signals into anticipatory hazard management, supporting climate‐resilient decision‐making in vulnerable regions.