Abstract: Using observation data from multiple S-band CINRAD radars in Guangdong Province from April 26—27, 2019, this study implemented a mesocyclone detection algorithm to compare and analyze identification results of the same meso-γ-scale vortices within the overlapping areas of multiple radars and proposed a fusion method for meso-γ-scale vortex detection by China’s new-generation weather radar network. The results show that: Vortex identification results are significantly affected by limited radar vertical resolution, terrain blockage, and radial velocity distance ambiguity, leading to variations in vortex counts and vortex characteristic parameters among different radars. When two radars have similar distances to a vortex, their identified parameters show good consistency. However, when the distance difference increases, the radar farther away tends to detect weaker rotational velocities, reduced shear, smaller diameters, higher base heights, and shallower depths due to radar resolution degradation. By incorporating distance weighting and height correction, the proposed fusion method effectively combines the advantages of multiple radars, overcoming limitations from distance differences and terrain blockage. This approach yields more accurate depth measurements, larger rotational velocities, shear and diameter, and an extended range for meso-γ-scale vortices. The research results contribute to improving the quality of identification results for medium-γ scale vortex and reducing observation errors between the same medium-γ scale vortex in the overlapping coverage area of different radars.