An observed fact is proposed that the 2020 record-breaking Meiyu over the Yangtze–Huaihe River Basin (YHRB) is characteristic of significant subseasonal variation. The anomalously enhanced rainfall experiences a southward retreat from the northern YHRB in June to the southern YHRB in July. The meridional shift of abnormal rainbelt is closely related to the different anomalous Meiyu front circulations. The first stage features a warm Meiyu front with an extremely strong northward transport of warm-and-wet air, whereas a cold front forms with a convergence between the strong northeasterlies and southwesterlies in July. Another significant subseasonal variation of the 2020 Meiyu is that the Meiyu rainfall evolves in a periodic quasi-biweekly oscillation (QBWO). On the scale of the QBWO, the cold air activities over East Asia contribute significantly to Meiyu; each intense southward invasion of cold air corresponds well to the enhancement of Meiyu rainfall. However, the location of the western Pacific subtropical high, the subtropical East Asia summer monsoon (SEASM), and the intensity of the East Asia subtropical westerly jet exhibit opposite QBWO characteristics in June and July. For an increasing (decreasing) of Meiyu rainfall, the western Pacific subtropical high marches northward (retreats southward), the subtropical East Asia summer monsoon weakens (strengthens), and the East Asia subtropical westerly jet accelerates (decelerates) in June. The opposite holds true in July. Further analyses reveal that the filtered cyclone over Northeast China benefiting the southward intrusion of cold air is associated with the eastward propagation of a wave train over the mid-high latitudes in June, whereas it arises from both the westward propagation of a wave train over the mid-high latitudes and the northward propagation of cyclonic circulation from the tropics in July.

This article investigates the climatic characteristics of Meiyu over the Yangtze-Huaihe River Basin and the primary impact system based on Meiyu onset data from 1954 to 2020, precipitation during the Meiyu Period, NCEP/NCAR reanalysis data, sea surface temperature (SST) data of NOAA, and western Pacific subtropical high (sub-high) data provided by the National Climate Center. The main analytical method used is synthetic analysis. The results indicate that the Meiyu onset date has enormous interannual variation. The date of the earliest Meiyu emergence is 27 days prior to the latest. There is a strong relationship between the duration of Meiyu and rainfall. The start of Meiyu correlates with the northward motion of the South Asia High, the northward jump of the westerly jet, the northward jump of the subtropical high, and the northward motion of the low-level jet. In the early Meiyu years featured in this study, the northward jump of the aforementioned systems occurred earlier and the position of the westerly jet shifted from 34–35°N to 38–40°N, which is much further north than its position under typical climatic circumstances. From late May to early June, the subtropical high was extraordinarily powerful, the ridgeline reached 20°N, and its position was further to the north. In years with a late Meiyu onset, the position of the westerly jet and the ridgeline were further south than the climatological average from early to late June; the jump to the north was not evident until late June. In addition, SST anomalies of the Indian Ocean and Pacific Ocean are significant external forcing variables that influence the Meiyu start date. The high SST of the equatorial Indian Ocean and the equatorial tropical eastern Pacific in the autumn of the preceding year and the high SST of the equatorial Indian Ocean in the spring of that year are prophase indicators of an early Meiyu start in the Yangtze-Huaihe River Basin.

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor Z_H ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity Z_DR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase K_DP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province.

Hourly precipitation is a standard variable for hydrological applications, flood warning, and quantitative assessment of precipitation-related hazards. Weather radar provides high-frequency observations, but these sub-hourly measurements must be linked to hourly rain-gauge accumulations for radar-based quantitative precipitation estimation (QPE). This study develops a machine-learning-based hourly radar QPE framework using intra-hour temporal features from dual-polarization observations. Each hourly gauge accumulation was matched with ten consecutive 6 min radar observations. Six temporal features were derived from dual-polarization observations, including the mean, sum, standard deviation, linear trend, maximum, and skewness. The results show clear rainfall regime dependence in the contribution of polarimetric variables. Evaluation of machine-learning models confirms the value of intra-hour temporal features, with the best-performing model achieving an RMSE of 3.96 mm h⁻¹, compared with 6.42 mm h⁻¹ for the linear baseline. The experiments further show that the complete temporal feature set improves QPE performance. Diagnostic evaluation also indicates that model bias is sensitive to the representation of heavy rainfall samples. These findings suggest that ML-based radar QPE should emphasize physically meaningful intra-hour polarimetric features and rainfall-imbalance-aware sampling.