Raindrop size distribution (DSD) is a basic feature of precipitation microphysics, and it plays a vital role in improving radar quantitative precipitation estimation and forecast accuracy. In South China, due to the influence of atmospheric circulation and weather systems, precipitation is frequent, and the types of precipitation and their temporal and spatial distributions are complex. It has become essential to study the types of precipitation in this area and their seasonal variation characteristics. Therefore, in this study, the DSDs in the dry and wet seasons of southern China are analyzed using measurements from seven ground-based two-dimensional video disdrometers (2DVDs) over the course of four years (2016–2019). The dynamic and microphysical characteristics of precipitation in the dry and wet seasons are established for South China, combined with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-generation Reanalysis (ERA5) data. Compared to the dry season results, the concentration of small raindrops (diameters <1 mm) in wet season is similar, while the concentration of midsized raindrops (diameters between 1 and 3 mm) and large raindrops (diameters >3 mm) in the wet season are significantly greater. Furthermore, taking the contributions to precipitation into account, the rainfall in the dry season is dominated by stratiform rain, which accounts for 59.3% of the total rainfall. Considering precipitation in the wet season, convective rain accounts for 77.9% of total the rainfall. The obtained relations of the shape parameter (μ) versus the slope parameter (Λ) (μ-Λ) and the radar reflectivity factor (Z) versus the rainfall rate (R) (Z-R) have obvious differences for different type rainfall in the two seasons. In the wet season, the water vapor is sufficient and the upward convection activity is intense in South China, causing the frequent occurrence of raindrop collisions and coalescence. This is also the reason that the D_m and R values in the wet season are greater than those in the dry season, while the wet season log₁₀N_w is relatively small. This not only deepens the understanding of the seasonal DSD variation in South China, but also provides theoretical support for improving numerical weather prediction models.