Modern antenna systems face serious problems of mutual coupling, which will cause electromagnetic compatibility problems such as pattern distortion. This article presents an innovative methodology for adaptive mutual coupling compensation based on the efficient characterization of coupled antenna arrays (CAAs), especially for nonuniform configurations. As a data-driven methodology, efficient characterization of CAAs is conducted firstly to simplify the analysis of mutual coupling by utilizing the characteristic modes of individual antenna elements, resulting in a more efficient data-collection process with reduced computational time. More importantly, this efficient characterization approach is applicable to any antenna spacing and excitation distribution, providing a new physical perspective to reveal mutual coupling. Furthermore, to realize adaptive mutual coupling compensation, this article employs a conditional generative adversarial network to address the challenges of high dimensional data loss and nonunique input-output mapping. The proposed methodology is demonstrated through its implementation in a real-world engineering scenario and provides a comprehensive solution for adaptive mutual coupling compensation. In other words, it avoids time-consuming full-wave simulations, complex neural network processing, and repetitive iterations.