The traditional early warning methods for offshore jacket structures heavily rely on single models, resulting in a high frequency of false alarms, which fail to meet the practical demands of engineering applications. To improve the accuracy and reliability of early warnings, this paper proposes a joint cumulative probability distribution-based early warning method utilizing early warning signals (EWS). This method first extracts the first-order autocorrelation coefficient and Lyapunov exponent from the platform's environmental monitoring and structural response data, and then predicts their residual sequence using the ARIMA model for subsequent analysis. Subsequently, to precisely characterize the nonlinear dependencies between different warning indicators, kernel density estimation is used to construct the marginal distribution functions. Based on the optimized Gumbel Copula function, a joint distribution of the residuals of the EWS indicators is established. The results show that the joint residuals exhibit weak correlation in the non-tail regions, but significantly stronger correlation in the tail regions, particularly in the upper tail. Finally, contour plots of the joint cumulative probability distribution are generated based on the 3σ criterion of probability statistics, and the points of intersection in different regions are analyzed to achieve multi-indicator joint early warnings. The case study shows that, due to the limitations of model structure and sequence characteristics, false alarm rates in single-indicator models can range from 11.49% to 12.08%. In contrast, the early warning model based on the joint cumulative probability distribution using Copula effectively integrates the probability distribution characteristics of EWS indicators, reducing the false alarm rate to 4.36%, thus improving the accuracy of early warnings. This approach provides valuable reference for the operational safety management of offshore jacket structures.