Abstract: Mesoscale vortices (MV) are among the most vital weather systems responsible for precipitation and meteorological disasters in China. However, there is currently no universal standard for MV identification, and the objective identification of MV remains an important problem to be addressed. Herein, according to the main features of MV in China, a new objective identification algorithm (referred to as the new algorithm), suited toward high-precision grid data, is developed by combining wind and vorticity data. The new algorithm can accurately identify mesoscale cyclonic circulation and locate the vortex center with a lower false rate and higher positioning accuracy than existing identification methods. The new algorithm is applied to three types of mesoscale vortices: Plateau vortex, Southwest Vortex, and Dabie Mountain vortex (DBV), which frequently occur along the Yangtze River basin. The results reveal that the new algorithm performs well for all three types of MV and is almost insensitive to the applied period or data resolution (using six-hourly 0.5°×0.5° NCEP CFSR reanalysis data and hourly 0.25°×0.25° ERA5 reanalysis data). Furthermore, the new algorithm is quantitatively evaluated using a DBV activity dataset for the warm season (May–September) from 1979 to 2020. The evaluations demonstrate that the new algorithm can stably identify MV over a long period of time, with an average hit rate of 95.5%. Additionally, this paper proposes a 3D MV tracking scheme, which offers remarkable advantages over traditional tracking methods.