Focusing on the forecasting and early warning of heavy precipitation in the key areas along the Sichuan-Xizang Railway, a heavy precipitation classification forecast model has been constructed for various subregions based on high spatiotemporal resolution Fengyun-4 (FY-4) satellite data and ERA5 reanalysis product for the summers of 2020—2024, combined with the Light Gradient Boosting Machine algorithm. The model's interpretability is analyzed using Shapley additive explanation (SHAP), and the distribution characteristics of key forecasting factors are analyzed. Results show that the model achieves a critical success index (CSI) of 0.41 for heavy precipitation forecast in the key regions with a probability of detection (POD) reaching 0.76, and demonstrates a strong forecasting capability. Regional model analysis indicates that the POD for heavy precipitation is 0.83 and the CSI is 0.53 in western Sichuan. The POD is 0.69 and the CSI is 0.33 in the southeastern region of the Qingzang Plateau, indicating significant regional differences in the forecasting. SHAP and statistical analysis show that heavy precipitation in the southeastern Qingzang Plateau is mainly dominated by satellite brightness temperature difference (BTD) factors (such as BTD_6.25−7.1 and BTD_13.5−10.7) that reflect variations in mid- and upper-level water vapor and cloud top height, while thermal instability parameters (such as CAPE (convective available potential energy), K index) and low-level vertical motion are the main indicators for heavy precipitation in western Sichuan. 60 min prior to the occurrence of heavy precipitation, key satellite parameters and physical parameters already exhibited statistically significant differences. Satellite parameters begin to show notable evolutionary characteristics as early as 150 min prior to the precipitation, providing quantitative reference for early warning. Based on interpretable machine learning methods, it is possible to not only enhance the objective forecasting ability of heavy precipitation, but also deepen the understanding of regional heavy precipitation mechanisms. This study provides scientific support for disaster prevention and reduction along the key areas of the Sichuan-Xizang Railway.

Summer precipitation over the Qinghai–Xizang Plateau (QXP) exhibits an uneven east–west distribution. The zo-nal shear line (ZSL) over the QXP is the primary synoptic system influencing summer precipitation, and it is the only synoptic system that can largely span the QXP from east to west, having obvious implications for the distribution of summer precipitation over the QXP. The proximity of the zonal distribution of precipitation to the ZSL and the rea-sons for this unique proximity deserve an in-depth investigation. Based on the fifth generation ECMWF Reanalysis (ERA5) hourly data in 1980–2019, 11 ZSL cases that caused heavy precipitation over the QXP were composited, and horizontal atmospheric motion was decomposed into rotational and divergent components to diagnose the atmosphe-ric dynamics responsible for the closeness of the precipitation and ZSL. The results demonstrate that obviously hea-vier precipitation lies in the eastern section of the ZSL (ZSL_ES, east of 88.5°E) than in the western section of the ZSL (ZSL_WS, west of 88.5°E). At upper levels, both the anticyclonic circulation and divergent wind intensity are stronger in the ZSL_ES than in the ZSL_WS, and a deeper convergence layer is present in the mid–lower troposphere of the ZSL_ES, resulting in upward air motion approximately twice the intensity of that in the ZSL_WS. The net water vapor flux of the whole layer near the ZSL_ES is 7.7 times that near the ZSL_WS, since water vapor is found to be transported mainly through the Yarlung Zangbo Grand Canyon and converge on the southern ZSL_ES. The stronger upward velocity lifts more water vapor near the ZSL_ES, resulting in notably greater precipitation than that observed in the ZSL_WS.

The C-vector (C) is an extension of the Q-vector (Q) in the three-dimensional space, characterizing three-dimensional ageostrophic motion. However, C is derived from the geostrophic wind and adiabatic approximations, and the diabatic effect is excluded. Precipitation and its impact on weather systems in the atmosphere are frequently related to the diabatic effect produced by the release of latent heat. Based on the primitive equations in p-coordinate with the β-plane approximation and the diabatic effect, the moist C-vector (C^*) is proposed, and its physical interpretations are revealed. A Qingzang plateau vortex case is diagnosed by C^* using the fifth-generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis data (ERA5) and the database of the Qingzang plateau vortex. Results are compared with the diagnostic results of C to prove the application value of C^*. It is found that compared with Q and C, C^* contains more comprehensive information of the ageostrophic motion and thus has more advantages in diagnosis. The horizontal component of C^* ( ${\mathit{C}}_{\text{hor}}^{\ast }$) depicts the secondary circulation caused by thermal wind imbalance and diabatic effect, better explaining the reason for the direction change of the Qingzang plateau vortex movement than that C. The vertical component of C^* ( $C_p^{\ast }$) restores the information of ageostrophic motion which is lost by Q, and depicts horizontal ageostrophic motion caused by geostrophic imbalance. The negative $C_p^{\ast }$ center represents the Qingzang plateau vortex center, and has a certain predictive effect on the development of the Qingzang plateau vortex. Besides, the relative sizes of ${\mathit{C}}_{\text{hor}}^{\ast }$ and $C_p^{\ast }$ indicate that the Qingzang plateau vortex has a significant baroclinic property.

From 17 to 22 July 2021, an extremely heavy rainstorm occurred in Henan province, China, causing severe casualties and property damages. This rainstorm is named as "21.7" extreme rainstorm. The "21.7" extreme rainstorm has attracted great attention of many scholars, and significant progress was made with nearly 100 studies of this flood in less than two years. The present study reviews recent research progresses in characteristics of the "21.7" extreme rainstorm, including influencing weather systems and their mechanisms, the effects of underlying surface and climate warming and numerical forecast, etc. These progresses are compared with research progresses of the "75.8" heavy rainstorm. Results indicate that compared with the "75.8" heavy rainstorm, finer characteristics of rainfall and mesoscale and microscale systems in the "21.7" extreme rainstorm have been revealed due to the development of observation technology and the improvement of research methods, particularly in terms of microphysical processes. Among these findings, the dynamic process of coupling enhancement between mesoscale convective system and mesoscale convective vortex and the microphysical process of simultaneous growth of various scale particles promoted by melting of hail particles in the "21.7" extreme rainfall process are important findings. The "21.7" extreme rainstorm was more strongly influenced by tropical and oceanic factors. With global warming, the "21.7" extreme rainstorm showed a more pronounced response to tropical atmospheric circulation and ocean than the "75.8" heavy rainstorm, which may result in more extreme hourly rainfall during the "21.7" extreme rainstorm. However, urban impact on the "21.7" extreme rainstorm is complex. It is challenging to distinguish urban impact from global warming signals, which increases the uncertainty regarding the mechanism of the "21.7" extreme rainstorm. Finally, directions of future research are discussed.

The Yangtze-Huaihe shear lines (YHSLs) generated in the eastern plain and the Tibetan Plateau shear lines (TPSLs) generated in the western Tibetan Plateau are located in the same latitude zone of subtropical East Asia. In order to deepen the understanding of the YHSLs and the TPSLs located at different terrain heights, based on the ERA-interim reanalysis data and composite analysis, a comparative study is carried out on the relationship between shear lines and rainstorms, three-dimensional structural characteristics of shear lines, characteristics of wind field and circulation near shear lines, and the thermal mechanism in the structural evolution of shear lines. The results show that: (1) The YHSLs can be divided into four types, namely warm type, cold type, quasi-stationary type and vortex type. The TPSLs are classified into horizontal TPSLs and vertical TPSLs. Both of them are closely related to rainstorms. In summer, nearly 70% of the YHSLs can produce rainstorms. Warm YHSL-induced rainstorms have the largest contribution to total rainfall of YHSL-induced rainstorms, while the vortex YHSL-induced rainstorms have the largest rainfall intensity but low occurrence frequency. Nearly 60% of the horizontal TPSLs bring rainstorms to the main area of the Tibetan Plateau, and more than 55% of the vertical TPSLs cause rainstorms to the east side of the Tibetan Plateau and its adjacent areas. (2) Both the YHSLs and the TPSLs are boundary-layer systems, and the characteristic levels are located at 850 hPa and 500 hPa, respectively. On the temporal and spatial scales, the horizontal dimensions of cold YHSLs and horizontal TPSLs can reach 1000 km and 2000 km, their vertical extension thicknesses can reach 5 km and 2 km, and their life spans can be up to 48 h and 96 h, respectively. Both of the YHSLs and TPSLs are inclined northward from lower to upper levels. (3) There are differences in wind field and circulation characteristics between cold YHSLs and horizontal TPSLs. The north side of a cold YHSL is northeasterly wind, and the south side is southwesterly wind; wind fields in the east and west of a horizontal TPSL are different as the west section is similar to cold YHSL and the east section varies significantly in different development stages. (4) There are differences in dynamic and thermal structures between cold YHSLs and horizontal TPSLs. In terms of dynamic structure, the YHSLs and horizontal TPSLs are all located in the positive vorticity zone, and the strength of the positive vorticity center reaches the maximum at the strong stage. In terms of thermal structure, a cold YHSL is near the low-level frontal zone, its western section is located in the warm and humid zone and its eastern sections is located in the dry and cold zone. The south side of a horizontal TPSL is highly warm and humid, and there is a frontal zone structure on the north side of the TPSL. (5) The diabatic heating near the shear line is closely related to the evolution of the cold YHSL and the horizontal TPSL. The vertical diabatic heating is the most important factor that causes the development and enhancement of the cold YHSL and the horizontal TPSL. There are differences in thermal structure and weakening mechanism between cold YHSLs and horizontal TPSLs. The invasion of dry and cold air will lead to the weakening or even extinction of a horizontal TPSL, while the weakening of the intensity of a cold YHSL is related to the northward invasion of warm and humid air from the south.

In order to study the relationship between cold vortex and tornado in Liaoning, the characteristics of tornado occurrence in Liaoning under the background of cold vortex are investigated. Using tornado observations and weather disasters data in Liaoning province and the ERA5 atmospheric reanalysis data of the European Center for Mediumrange Weather Forecasts (ECMWF) from 1951 to 2020, tornado cases in Liaoning under the cold vortex background are collected and sorted out. Differences between the physical parameters of EF2—4 (EF2+) and EF0—1 (EF1−) tornadoes under the cold vortex background are compared. The results are as follows. (1) Tornadoes under the cold vortex background mainly occur in the coastal area and the central and northern plain of Liaoning province. The average distance between the tornado and the center of the cold vortex is nearly 900 km. The stronger the cold vortex, the farther its center is from the tornado; the weaker the cold vortex, the closer the distance between the center of the cold vortex and the tornado. Under the background of cold vortex, tornadoes in Liaoning province mainly occur between the southeast and southwest quadrants of the cold vortex, and frequently occur in the enhancement stage of the cold vortex. (2) The number of tornados in Liaoning province in the cold vortex background accounts for nearly 50% of the total samples from 1951 to 2020, and the number of tornados is positively correlated with the number of cold vortex days. The distance between the mean location of the tornadoes in Liaoning under the background of the cold vortex and the average center of the cold vortexes has obvious monthly changes with the closest distance in August. (3) Comparing EF2—4 (EF2+) with EF0—1 (EF1−) tornadoes under the background of cold vortex, the thermal and dynamic environmental parameters of EF2+ tornadoes are 40% and 65% higher than that of EF1− tornadoes. Comparing the tornadoes in Liaoning under the background of cold vortex and tropical cyclone tornadoes, it is found that the convective available potential energy of the cold vortex tornadoes is about 3 times that of the tropical cyclone tornadoes, but the storm relative helicity of the cold vortex tornadoes is only half that of the tropical cyclone tornado. (4) Energy helicity index (EHI) and Significant tornado parameter (STP) can be used to determine the tornado level in Liaoning under the cold vortex background, which is far below the comprehensive parameter threshold of the American tornado. Therefore, the intensity and position of the cold vortex are closely related to the occurrence of tornado in Liaoning. The physical quantity parameters of the EF2+ tornado under the cold vortex background are significantly greater than those of the EF1− tornado. The thermal and dynamic environmental parameters of tornado in Liaoning under the background of cold vortex and tropical cyclone tornado are quite different. Adjusting the Energy helicity index and Significant tornado parameter thresholds can improve the POD of strong tornado in Liaoning under the background of cold vortex.

Characteristics and water vapor condition of the extreme rainstorm in North China from 29 July to 1 August in 2023 are investigated based on surface meteorological observations of precipitation and the ERA5 reanalysis data. It is found that the “23·7” extreme rainstorm in North China has the characteristics of long precipitation period and large cumulative amount, exhibiting a significant extremity. The precipitation was mainly located in front of the Taihang mountain and the Yanshan mountain. The maximum precipitation center was basically consistent with the orientation of the mountains, showing a banded pattern. There were significant circulation anomalies during the extreme rainstorm. The northward shifted upper-level subtropical jet stream and the abnormally northerly western Pacific subtropical high pressure were the key circulation factors that affected the extreme rainstorm, while the remnant circulation of northward-moving typhoon Doksuri, typhoon Khanun and the low-level jet stream were the major weather systems affecting the extreme rainstorm. The water vapor condition of the extreme rainstorm showed obvious phase features. From 08:00 BT 29 to 08:00 BT 31 July, low-level convergence, high-level divergence and upward motion were strong in the main precipitation area, and the low-level cyclonic wind further deepened. Water vapor was transported by the remnant circulation of typhoon Doksuri over a short range and continued by typhoon Khanun, with the former being the main source. The entire layer of water vapor in Beijing-Tianjin-Hebei region was in a net inflow state, with the maximum net inflow reaching 1.5×10⁸ kg/s. From 08:00 BT 31 July to 08:00 BT 1 August, the ascending motion, the lower-level convergence and upper-level divergence significantly weakened, the thickness of low-level cyclonic wind field shrank, and water vapor was transported only by typhoon Khanun over long distances. The entire layer of water vapor in Beijing-Tianjin-Hebei region was in a state of net outflow, with the maximum net outflow reaching 5×10⁷ kg/s. The blocking effect of the Taihang mountain and Yanshan mountain caused the water vapor convergence center to remain for a long time, and their frictional effect may be favorable for the amplification of the ascent motions in front of the mountain. These effects provided favorable conditions for this extreme heavy rainfall.

Accurate, reliable, and high spatiotemporal resolution precipitation products are essential for precipitation research, hydrological simulation, disaster warning, and many other applications over the Tibetan Plateau (TP). The Global Precipitation Measurement (GPM) data are widely recognized as the most reliable satellite precipitation product for the TP. The China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) precipitation fusion dataset (CLDAS-Prcp), hereafter referred to as CLDAS, is a high-resolution, self-developed precipitation product in China with regional characteristics. Focusing on the TP, this study provides a long-term evaluation of CLDAS and GPM from various aspects, including characteristics on different timescales, diurnal variation, and elevation impacts, based on hourly rain gauge data in summer from 2005 to 2021. The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP. They both perform well for precipitation amount and frequency on multiple timescales. CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP. However, GPM tends to overestimate both precipitation amount and frequency. The difference between them mainly lies in the trace precipitation. CLDAS and GPM effectively capture rainfall events, but their performance decreases significantly as intensity increases. They both show better accuracy in diurnal variation of precipitation amount than frequency, and their performance tends to be superior during nighttime compared to the daytime. Nevertheless, there are some differences of the two against rain gauge observations in diurnal variation, especially in the phase of the diurnal variation. The performance of CLDAS and GPM varies at different elevations. They both have the best performance over 3000–3500 m. The elevation dependence of CLDAS is relatively minor, while GPM shows a stronger elevation dependence in terms of precipitation amount. GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations. CLDAS and GPM exhibit unique strengths and weaknesses; hence, the choice should be made according to the specific situation of application.