An effective modeling method for nonlinear distributed parameter systems (DPSs) is critical for both physical system analysis and industrial engineering. In this paper, we propose a novel DPS modeling approach, in which a high-order nonlinear Volterra series is used to separate the time/space variables. With almost no additional computational complexity, the modeling accuracy is improved more than 20 times in average comparing with the traditional method. Introduction -Most of the physical processes (e.g. thermal diffusion process [1,2,3,4,5,6,7], thermal radiation process [8], distributed quantum systems [9,10], concentration distribution process [11,12,13], crystal growth process [1, 6], etc.) are nonlinear distributed parameter systems (DPSs) with boundary conditions determined by the system structure. Thus, it is an urgent task to design an effective modeling method for nonlinear DPSs. The key problem in the design of nonlinear-DSP modeling method is how to separate the time/space variables. Some modeling approaches are previously proposed: These include the Karhunen-Loève (KL) approach [1,4,14,15], the spectrum analysis [16], the singular value decomposition (SVD) combined with the Galerkin's method [1,17], and so on. Among them, the KL approach is the most extensively studied and the most widely applied one. In this approach, the output T (z, t) is expanded as