Adjoint sensitivity analysis is an exceptionally efficient method for computing the gradient of an objective function with respect to given parameters, playing a crucial role in modern circuit design and verification. According to the principles of the adjoint method, it is necessary to store all essential system state information, such as state vectors and Jacobian matrices, at each time step during the forward integration process in order to construct the adjoint equations during the backward integration. Therefore, the memory overhead of the adjoint method is proportional to the system size and the number of time steps, resulting in prohibitive memory costs for solving large-scale dynamic systems.In this paper, we propose a novel, memory-efficient adjoint sensitivity analysis method that significantly reduces the memory overhead of storing system state information by employing error-bounded lossy compression techniques. Our compression algorithm effectively utilizes the spatiotemporal characteristics of data in circuit simulations and incorporates stringent error control mechanisms. This approach achieves a two-order-ofmagnitude reduction in memory overhead during simulation while ensuring that the accuracy of the adjoint solution remains unaffected.