we study the decomposition methods for solving a class of nonconvex and nonsmooth two-stage stochastic programs, where both the objective and constraints of the second-stage problem are nonlinearly parameterized by the first-stage variable.  due to the failure of the clarke-regularity of the resulting nonconvex recourse function, classical decomposition approaches such as benders decomposition and (augmented) lagrangian-based algorithms cannot be directly generalized to solve such models. by exploring an implicitly convex-concave structure of the recourse function, we introduce a novel surrogate decomposition framework based on the so-called partial moreau envelope. convergence for both fixed scenarios and interior sampling strategy is established. numerical experiments are conducted to demonstrate the effectiveness of the proposed algorithm.