<p>This study utilized continuous observational data from a PARSIVEL2 disdrometer collected during winter from 2019 to 2021 in the southwest mountainous areas of China. Based on the diameter and terminal fall velocity of the precipitation particles, combined with the discrete Fréchet distance method, the precipitation particles were classified into five categories: freezing raindrops, freezing raindrops-graupel mixed (F-G Mixed), graupel, graupel-snow mixed (G-S Mixed), and snow. The characteristics of their particle size distributions (PSDs) were analyzed, and the results indicated that during freezing weather, the dominant precipitation type was G-S Mixed, accounting for 44.80% of total precipitation. The total number concentration (<i>N</i>_t), mass-weighted mean diameter (<i>D</i>_m), and spectrum dispersion (<i>σ</i>) of all precipitation particles exhibit a positive correlation with precipitation intensity (PI), while the normalized intercept parameter in logarithmic form (log₁₀<i>N</i>_w) shows minimal correlation with PI. Particles with diameters smaller than 2 mm contributed significantly to <i>N</i>_t, with freezing raindrops, F-G Mixed, and graupel particles between 1 mm and 2 mm, and G-S Mixed and snow particles larger than 4 mm contributing the most to PI. The mean PSD width followed the order of snow &gt; G-S Mixed&gt; graupel &gt; freezing raindrops &gt; F-G Mixed. Furthermore, this study derives the shape (<i>μ</i>) and slope (<i>Λ</i>) parameters of the Gamma distribution for different precipitation types, as well as the relationships between radar reflectivity (<i>Z</i>) and PI, and between kinetic energy (KE) and PI. These findings are expected to enhance the accuracy of PSD retrieval and the quantitative estimation of winter precipitation in this area.</p>