Abstract: The Generative Adversarial Network (GAN) is an important thought and method in the field of artificial intelligence. A generation method of wind fields is proposed in this paper based on GAN. To overcome the lacking of on-site measurements of wind field data, the modified precursor method is firstly used to generate the training data in GAN (i.e. GAN_θ and GAN_Δθ). The former model (GAN_θ) is used to generate single-point phase spectrum while the latter (GAN_Δθ) is to create phase spectrum difference per unit distance. This can overcome the disadvantages of slightly lower turbulence and distortion of spectral characteristics in traditional simulation methods. Based on the Center Progressive method, the phase spectrum is obtained from the results of GAN_θ and GAN_Δθ models. Wind fields are generated by the phase spectrum and amplitude spectrum. The quality of GAN-based results is evaluated qualitatively and quantitatively through data distribution and 1-NN algorithm, respectively. Furthermore, the characteristics of wind fields predicted by GAN are compared with those of the target wind fields. The evaluation and comparison results show that the data distribution generated by GAN agrees well with the target distribution, and the characteristics of generated wind fields are in a good agreement with those of target wind fields. This indicates that the GAN-based generation method of wind fields, by generating data through learning data distribution, has good adaptability and capability of data generation.based generation method of wind fields, by generating data through learning data distribution, has good adaptability and capability of data generation.