This paper presents the background, scientific objectives, experimental design, and preliminary achievements of the Third Tibetan Plateau (TP) Atmospheric Scientific Experiment (TIPEX-III) for 8–10 years. It began in 2013 and has expanded plateau-scale observation networks by adding observation stations in data-scarce areas; executed integrated observation missions for the land surface, planetary boundary layer, cloud–precipitation, and troposphere–stratosphere exchange processes by coordinating ground-based, air-based, and satellite facilities; and achieved noticeable progress in data applications. A new estimation gives a smaller bulk transfer coefficient of surface sensible heat over the TP, which results in a reduction of the possibly overestimated heat intensity found in previous studies. Summer cloud–precipitation microphysical characteristics and cloud radiative effects over the TP are distinguished from those over the downstream plains. Warm rain processes play important roles in the development of cloud and precipitation over the TP. The lower-tropospheric ozone maximum over the northeastern TP is attributed to the regional photochemistry and long-range ozone transports, and the heterogeneous chemical processes of depleting ozone near the tropopause might not be a dominant mechanism for the summer upper-tropospheric–lower-stratospheric ozone valley over the southeastern TP. The TP thermodynamic function not only affects the local atmospheric water maintenance and the downstream precipitation and haze events but also modifies extratropical atmospheric teleconnections like the Asia–Pacific Oscillation, subtropical anticyclones over the North Pacific and Atlantic, and temperature and precipitation over Africa, Asia, and North America. These findings provide new insights into understanding land–atmosphere coupled processes over the TP and their effects, improving model parameterization schemes, and enhancing weather and climate forecast skills.

Fine particulate matter (PM_2.5) poses a risk to human health. In January 2017, the PM_2.5 pollution in China was severe, and the average PM_2.5 concentration had increased by 14.7% compared to that in January 2016. Meteorological conditions greatly influence PM_2.5 pollution. The relationship between PM_2.5 and meteorological factors was assessed using monitoring data and the Community Multiscale Air Quality modeling system (CMAQ) was used to quantitatively evaluate the impacts of variations of meteorological conditions on PM_2.5 pollution. The results indicate that variations of meteorological conditions between January 2017 and January 2016 caused an increase of 13.6% in the national mean concentration of PM_2.5. Unlike the Yangtze River Delta (YRD), where meteorological conditions were favorable, unfavorable meteorological conditions (such as low wind speed, high humidity, low boundary layer height and low rainfall) contributed to PM_2.5 concentration worsening by 29.7%, 42.6% and 7.9% in the Beijing-Tianjin-Hebei (JJJ) region, the Pearl River Delta (PRD) region and the Chengdu-Chongqing (CYB) region, respectively. Given the significant influence of local meteorology on PM_2.5 concentration, more emphasis should be placed on employing meteorological means to improve local air quality.

Extreme precipitation events over the North China Plain (NCP) in midsummer during 1979–2016 are classified into two types using objective cluster analysis: a northern pattern with heavy precipitation and a central–southern pattern with relatively moderate precipitation. The large‐scale circulation patterns responsible for the midsummer extreme precipitation are then determined. In the northern NCP type, extreme precipitation accompanies a zonal gradient between an anomalous low‐pressure system at high latitudes and the westward‐ and northward‐extended western North Pacific subtropical high (WNPSH). Anomalous southwesterlies flow is driven by a trough that extended from the high latitudes to the northern NCP, where it encounters southeasterly wind flow induced by an anomalously northward‐extended WNPSH and a southern low‐pressure anomaly at low latitudes. Anomalous amounts of moisture are mainly transported from the tropical western Pacific by southeasterlies. In the central–southern NCP type, remarkable anomalous low‐pressure systems control all of northern China with centers over the Sichuan Basin and northeast China. The westward‐extended WNPSH occupies further south than that of the northern NCP type. The southwesterly low‐level jet (LLJ) is more prevalent in the central–southern NCP type than in the northern NCP type. This southwesterly LLJ plays an important role in extreme precipitation over the central–southern NCP by transporting moisture primarily from the Bay of Bengal and the South China Sea and generating convergence. In addition, the upper‐level anomalous strong divergence that is anchored over the right entrance of the westerly jet makes a greater contribution to extreme precipitation in the northern type than in the central–southern type.

This study aims to detect atmospheric rivers (ARs) around the world by developing a deep‐learning ensemble method using AR catalogs of the ClimateNet data set. The ensemble method, based on 20 semantic segmentation algorithms, notably reduces the bias of the testing data set, with its intersection over union score being 1.7%–10.1% higher than that of individual algorithms. This method is then applied to the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets to quantify AR frequency and its related precipitation in the historical period (1985–2014) and future period (2070–2099) under the Shared Socioeconomic Pathways 5–8.5 warming scenario. The six key regions, which are distributed in different continents of the globe and greatly influenced by ARs, are particularly highlighted. The results show that CMIP6 multi‐model mean with the deep‐learning ensemble method reasonably reproduces the observed AR frequency. In most key regions, both heavy precipitation (90–99 percentile) and extremely heavy precipitation (>99 percentile) are projected to increase in a warming climate mainly due to the increased AR‐related precipitation. The AR contributions to future heavy and extremely heavy precipitation increase range from 145.1% to 280.5% and from 36.2% to 213.5%, respectively, indicating that ARs should be taken into account to better understand the future extreme precipitation changes.