Characteristics of extreme precipitation influenced by Northeast China Cold Vortex and the possible mechanism of its interaction with northward typhoons by a numerical study

Abstract: Northeast China cold vortex (NEC-CV) plays an important role in modulating the extreme precipitation and have dramatic socioeconomic impacts over Northeast China. In this study, it is found that the extreme precipitation related to NEC-CVs can explain a considerable proportion (35%–40%) of total extreme precipitation over Northeast China and a more pronounced impact of the extreme precipitation related to NEC-CVs can be found for more extreme precipitation. The interaction between the typhoon and the NEC-CV contributes significantly for the increase of extreme precipitation. During 2001-2020, among the 39 northern typhoons affecting Northeast China, 82% triggered rainfall due to peripheral moisture, and 18% passed through Jilin Province. Under the background of cold vortex, 83.3% of the 12 northward typhoons caused heavy precipitation. Among the typhoons with heavy precipitation, 60% had daily precipitation reaching rainstorm and heavy rainstorm levels, and 70% had hourly rainfall intensity reaching short-term heavy precipitation levels (divided into steady and short-term types). Under the background of cold vortex, the high-value areas of northern typhoon track density were mainly distributed in the region of 130°E-140°E and 21°N-30°N. The northern tracks that caused heavy precipitation could be divided into four categories, with the northern-northward track being the most common (more than half) but with slightly weaker rainfall levels compared to other track types, while the northern-eastward track had the highest rainfall levels. Furthermore, this study evaluates the WSM6 (single-moment) and LIUMA (double-moment) microphysics schemes in CMA-MESO for simulating a cold vortex–typhoon induced heavy rainfall event in Northeast China in July 2023. Both schemes captured the event, but LIUMA showed better agreement with observations: higher correlation (0.75 vs. 0.70), lower RMSE (0.67 vs. 1.15 mm h⁻¹), and more realistic raindrop size distributions. WSM6 overestimated precipitation due to stronger latent heating (2.2 × 10⁻⁴ K s⁻¹ vs. 2.0 × 10⁻⁴ K s⁻¹), enhancing convection. LIUMA produced higher ice- and liquid-phase mixing ratios—especially excessive ice—which led to overly strong simulated radar reflectivity. Key differences stem from how each scheme treats ice-phase processes and ice–liquid interactions, highlighting the need for advanced cloud observations for further refinement.