Offshore riser systems are subjected to wind, wave and current loadings, which are random in nature. The structural analysis of offshore riser systems requires long simulation time and high computational resources. Structural reliability method, as an analysis tool to quantify probability of failure of components or systems, can account for uncertainties in environmental conditions and system parameters. It is particularly useful in cases where limited experience exists, or a risk-based evaluation of design is required. Monte Carlo Simulation (MCS) method is the most widely accepted method and usually used to benchmark other proposed reliability methods. However, it is computationally demanding for predicting low failure probabilities, especially for offshore dynamic problems involving many types of uncertainties. To tackle the computational burden of MCS method for structural analysis, a new approach for structural reliability analysis is proposed. The proposed approach is a probability density estimation-based method, which could solve nonlinear and high-dimensional problems, particularly when involving multiple design points, it also does not require coupling with finite element analysis, which is difficult and sometimes impossible to implement. To avoid coupling, post-processing methods offer an attractive mean to efficiently recover the probability density function (PDF). The accuracy and efficiency of the proposed approach are demonstrated by an offshore drilling riser system example.