The El Niño–Southern Oscillation (ENSO) represents the most prominent interannual signal within the global ocean–atmosphere system and stands as one of the crucial factors influencing the interannual variability of summer precipitation in the Beijing–Tianjin–Hebei (BTH) region. Based on data such as spring SST (Sea Surface Temperature), summer precipitation in the BTH region, and atmospheric circulation from 1961 to 2023, this study analyzes the interdecadal variation in the relationship between spring ENSO and summer precipitation in the BTH region as well as its possible causes. Results show that the key spring ENSO region significantly impacting the interannual summer-precipitation anomalies in the BTH region underwent an interdecadal shift in the late 1990s, migrating from the eastern Pacific in the earlier period to the central Pacific in the later period. Although SST anomalies (SSTAs) in the key spring ENSO region exhibit a strong negative correlation with the summer precipitation anomalies in the BTH region before and after the shift, the underlying mechanisms differ significantly. When SSTAs exist in the key spring ENSO region, the summer SSTAs in the North Pacific, Indian Ocean, and Atlantic Ocean show different characteristics, which in turn cause circulation anomalies in the tropics and mid-high latitudes through the synergistic effect of SSTAs in different regions. When the key spring ENSO region exhibits warm SSTAs, a distinct anticyclonic circulation is observed in the subtropical northwestern Pacific. After the interdecadal shift, this anticyclonic circulation strengthens, but it still fails to transport water vapor to the BTH region. The BTH region is controlled by a cyclonic circulation. In the earlier period, this cyclonic circulation is associated with SSTAs in the North Pacific, and in the later period, it is caused by the zonal wave train over the Eurasian continent, excited by SSTAs in the North Atlantic. The control of anomalous northerly winds over the BTH region reduces precipitation.

The drivers and mechanisms of the decadal change in summer precipitation over eastern China have been widely investigated. In this context, the potential impact of vegetation changes on the Qinghai–Xizang Plateau (QXP) and its associated processes remain unclear. Using the GIMMS (Global Inventory Modeling and Mapping Studies) Leaf Area Index (LAI) dataset and gridded precipitation data over China, this study investigated the relationship between the decadal increase in spring vegetation cover in the QXP and the decadal change in summer precipitation over eastern China, and the associated mechanism. The results revealed that the late-spring (May) vegetation in the QXP has undergone notable decadal changes around 2010, with LAI anomalies turning from negative to positive. Concurrently, there has been a noticeable decadal change in the summer precipitation over eastern China after 2010, with a shift from wet to dry situations over the Huaihe River basin and South China, and precipitation anomalies turning from negative to positive over Northeast China, North China, and the middle and lower reaches of the Yangtze River basin. A composite analysis of the changes in the surface energy budget over the QXP and the East Asian summer monsoon circulation showed that, as the vegetation cover in the QXP increased in the late spring after 2010, the net surface shortwave radiation increased in response to the low-albedo characteristics of vegetation, leading to an increase in the surface temperature over the QXP and consequently enhancing the upward surface sensible heat flux. The stronger spring sensible heat flux provides a stronger atmospheric heating source in the subsequent summer, intensifying and extending the South Asian High eastward. This effect is accompanied by the strengthening and westward movement of the Western Pacific Subtropical High, thereby changing the decadal summer precipitation over eastern China around 2010. Thus, this study has elucidated the important modulating role of vegetation change in the QXP on the decadal change in summer precipitation over eastern China.

Aircraft turbulence is primarily caused by atmospheric turbulence, which is closely related to changes in wind speed, temperature, and topography. This phenomenon is especially pronounced in the southwestern region, where the complex terrain and the significant uplifting effect of the plateau on air currents make aircraft highly susceptible to turbulence. The turbulence event experienced by flight MU5954, operating from Tengchong to Kunming on 4 April 2024, was analyzed using reanalysis data, topography data, satellite data, and aircraft in-flight reports. The results showed that the turbulence occurred near the boundary between the windward and leeward slopes of the mountains, where the air currents were unstable. The alternating positive and negative distributions of vertical velocity, divergence, and vorticity in the upper-level jet stream, combined with the convergence of cold and warm airflows, jointly triggered atmospheric turbulence, making the aircraft susceptible to turbulence during flight.

As urbanization accelerates, hazards including flash floods, debris flows, and urban inundation are becoming more frequent and severe, with escalating social disruption and economic losses. A scientific and effective radar quantitative precipitation estimation (QPE) product is essential for enhancing the monitoring, forecasting, and early warning of extreme precipitation, which is of great value to current disaster prevention and mitigation efforts. This study used data from a single S-band dual-polarization radar and six X-band dual-polarization phased array radars in Jiaxing to develop S-band and X-band radar QPE products for the region. After preprocessing the radar observation data, including removing nonmeteorological echoes and performing quality control of the differential propagation phase, the effective minimum hybrid elevation for each radar was calculated. Then, the rain rate was estimated using single radar observations and raindrop size distribution parameters provided by the Dual-frequency Precipitation Radar onboard the Global Precipitation Measurement satellite (GPM-DPR). Finally, the rain rate was mosaicked for multiple radars, forming a gridded QPE product with a temporal resolution of 1 min and a spatial resolution of 50 m. To verify the accuracy of the proposed method, two heavy precipitation events that occurred on 10 and 16 September 2024, in the Jiaxing area were selected to evaluate the radar QPE products. The evaluated products included the original Jiaxing X-band QPE product and the X-band, S-band, and mosaic products developed in this study. The results show that the proposed QPE products outperform the original X-band product in terms of accuracy and stability, with the mosaic product yielding the best estimation result. For localized heavy rainfall with horizontally inhomogeneous distribution, the mosaicking product accurately reflects the precipitation distribution across various towns (subdistricts), contributing to the early issuance of accurate warnings.

The China Meteorological Administration Mesoscale Model (CMA-MESO) serves as a critical operational system for precipitation forecasting. However, its eight-times-daily rolling updates present new challenges for forecast evaluation. A comprehensive understanding of mean bias characteristics, variations across different forecast lead times, and error sources is key to model improvement. This study systematically evaluated the precipitation forecast performance of the CMA-MESO over East China during the summer of 2023. The key findings are as follows: (1) While the averaged multiple forecasts generally reproduced the spatial distribution patterns of observed precipitation, systematic biases existed in precipitation intensity and frequency. (2) The model significantly overestimated the precipitation amount for moderate-and-above rainfall events, with 3.7% underestimation of the frequency of no-rainfall events but 2.6% overestimation of the frequency of light rain events, along with elevated false alarm rates for heavy-and-above rainfall events. (3) The forecast performance showed a distinct lead-time dependence, with precipitation biases largest in 3～6 h nowcasting, gradually decreasing to a minimum in 21～24 h forecasts, and then increasing again in the 24～36 h range. (4) Physical analysis revealed that the nowcasting overestimation stemmed from excessive hydrometeor introduction during cloud analysis, and the overestimation in the forecast after 24 h resulted from excessive low-level wind speeds that promoted moisture transport to the target region.

This study employs ERA5 reanalysis data, station precipitation data from the China Meteorological Administration, NOAA monthly average SST (sea surface temperature), and outgoing longwave radiation flux data from 1980 to 2020 to analyze the impact of zonal position of EASJ (East Asian subtropical jet) on summer precipitation in China, as well as its response to SST in the tropical central-eastern Pacific. The results indicate that in westward-shift years of the EASJ, upper-level convergence and lower-level divergence play an important role in YRV (Yangtze River valley). The eastward displacement and weakening of WPSH (western Pacific subtropical high) at 500 hPa suppress moisture transport to the YRV, leading to reduced precipitation. Meanwhile, upper-level divergence over the Hetao area promotes rising motion, accompanied by lower-level moisture convergence, increasing precipitation in the Hetao area. Meanwhile, in eastward-shift years of the EASJ, the WPSH strengthens and shifts westward. The convergence and divergence patterns in the upper and lower troposphere, along with moisture conditions and vertical motion in these two regions, exhibit reversed characteristics compared with those in westward-shift years of the EASJ. As a result, the precipitation anomalies of eastward-shift years of the EASJ display an opposite phase distribution compared with in westward-shift years of the EASJ. The EASJ’s zonal position is closely related to the summer SST in the tropical central-eastern Pacific. In this region, negative (positive) SST anomalies directly cool (warm) the atmosphere, suppressing (enhancing) local convective activity. These alterations modulate Walker circulation and lead to anomalous ascending (descending) motion in the tropical western Pacific. The vertical motion anomalies further affect East Asia’s mid-latitude region through meridional circulation, reducing (intensifying) convective activity near 30°N. Consequently, the meridional temperature gradient in the eastern part of the climatological jet region decreases (increases) due to diabatic heating, weakening (strengthening) the tropospheric westerlies and resulting in an anomalous westward (eastward) EASJ.

Millimeter-wavelength cloud radar serves as a pivotal instrument for cloud microphysical characterization but is susceptible to clear-air echoes induced by insects, birds, and aerosols, leading to considerable retrieval biases. To enhance detection accuracy and generalizability, this study presents ATNN (Multi-Head Attention Neural Networks), a universal deep learning framework that exclusively employs the reflectivity factor, radial velocity, and relative height for adaptive learning of vertical structural features. Evaluated on observations from Baise, Guangxi, ATNN demonstrates robust performance during the rainy season, achieving a clear-air echo detection rate of 91.98% and a meteorological echo preservation rate of 94.28%; during the non-flood season transition, these metrics are 87.56% and 86.38%, respectively. In contrast, conventional neural networks (NN) achieve marginally higher detection rates but misclassify numerous low-altitude, weak meteorological echoes as clear-air signals, yielding a preservation rate of only 54.96% in non-flood periods. Nationwide validation across 15 operational radar sites further confirms that ATNN attains an average clear-air echo removal rate of 81.17% while preserving 93.89% of meteorological echoes, thereby demonstrating strong regional adaptability.

In the context of global warming, the spatiotemporal correlation between extreme drought and heatwave events has intensified over time. Their compounding effects not only amplified their individual impacts but also exacerbated the pressure on water resources, agriculture, and ecological environments. Based on the fifth-generation reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5), this study calculated the standardized precipitation evapotranspiration index (SPEI) and the standardized temperature index (STI) and employed the Frank Copula function to construct the standardized compound dry and hot index (SCDHI). This approach systematically revealed the spatiotemporal evolution characteristics of compound hot–dry events in the Mongolian Plateau between 1982 and 2023. Additionally, the study integrated a random forest regression model to identify and quantify the key climatic factors that influenced compound hot–dry events in the region. The results indicated that over the past four decades, the intensity, the time proportion, and the affected area proportion of compound hot–dry events in the Mongolian Plateau exhibited a significant decreasing trend. Among the climatic variables, STI was the most influential factor affecting the compound events, with a feature importance value of approximately 0.5. This was followed by SPEI, temperature, and potential evapotranspiration (PET), with feature importance values of 0.4, 0.1, and 0.1, respectively, while precipitation had a relatively weak impact. This study underscored the need to incorporate the long-term impacts of compound hot–dry events into the protection and management of the grassland ecosystem of the Mongolian Plateau. It emphasized the importance of developing adaptive management strategies based on scientific predictions and risk assessments to enhance the resilience and sustainability of the ecosystem.

Southwestern China experienced a typical compound heat–drought event between mid-August and mid-September 2024, which significantly impacted the local economy and livelihoods. In this region, such compound events are related to intraseasonal oscillations (ISOs) originating from different areas. However, conventional statistical methods fail to distinguish the specific contributions of various ISOs to these extreme conditions. Using atmospheric-circulation diagnostics and numerical simulations using the Weather Research and Forecasting model with varying-ISO-signal-driven boundary forcings, this study reveals the following: the ISO signal induced by a mid-latitude westerly wave train in the upper troposphere increased the regional average temperature by 1.331°C in Southwestern China, primarily affecting the northern part of the region. This ISO signal accounted for 62.8% of the overall compound heat–drought event. The ISO signal associated with a middle-tropospheric teleconnection wave train increased regional average temperatures by 0.768°C. This impact is mainly concentrated in the eastern Sichuan Basin and most of Chongqing, accounting for 37.2% of the event. The development of this compound event was predominantly influenced by a mid-latitude westerly wave train from the northwestern boundary. This promoted the significant expansion of the South Asian High over Southwestern China, resulting in strong positive geopotential height anomalies. Simultaneously, warm advection due to the westward extension and northward shift of the Western North Pacific Subtropical High, coupled with positive tropospheric geopotential height anomalies, contributed to regional temperature increases and decreased soil moisture. Overall, this study enhances the understanding of the mechanisms driving compound heat–drought events in Southwestern China, providing a foundation for improving subseasonal-prediction capabilities.

Two large-hail supercells that occurred before spring in Zhejiang, on February 21, 2024 (“24·2·21”), and March 3, 2025 (“25·3·3”), were analyzed using the reanalysis data, automatic weather station observations, lightning location data, and S-band dual-polarization radar data. The results indicate that both convection events occurred after the passage of a cold wave front and were elevated convection systems. Above the inversion layer, conditionally unstable stratification and positive convective available potential energy were present. Deep convection was rapidly triggered under the influence of the 850 hPa shear line. Strong, deep-layer vertical wind shear favored the development of supercells. The dry layer in the “25·3·3” case was deeper than that in the “24·2·21” case. In both supercell hail processes, hailfall was accompanied by a decrease in storm centroid height and in the maximum shear height of the mesocyclone. The “24·2·21” supercell exhibited a long maintenance time, a mid-level double vortex structure, and differential reflectivity (Z_DR) and specific differential phase (K_DP) columns extending above 6 km. The Z_DR and K_DP values within the overhanging echo were small, and the cross-correlation coefficient (C_C) decreased with decreasing height, indicating the presence of melting small ice phase particles, which favored moist hail growth. In contrast, the “25·3·3” supercell had a shorter maintenance time, a deep single vortex (mesocyclone) structure, and a pronounced rear inflow. The Z_DR and K_DP columns were confined below 5 km. Small Z_DR and K_DP values accompanied by large C_C values at upper levels indicated a greater abundance of dry ice phase particles. This was manifested by an increased frequency of intracloud lightning flashes, which may be associated with accelerated freezing caused by rear-inflow dry cold air and was conducive to dry hail growth in the upper cold cloud region.