When assessing the socio‐economic impacts of climate change, it is sensible to make targeted climate projections for regions of high population density and economy activity. Much of human activity is concentrated at river basins, yet it has been difficult to resolve the complex boundaries of these basins in coarse resolution global climate models. The latest high‐resolution observation and climate projection datasets enable such basin‐based evaluations now, and this study assesses the historical and projected climate changes over three major river basins in China—the Yellow, Yangtze and Pearl River basins. Based on CN05.1 dataset, the Yellow River basin has significantly warmed by about 1.8°C over the past five decades, far more than the other two basins. The change in temperature extremes has been as severe, with the annual maxima of daily maximum temperatures (TXx) increasing by 1.5°C, and the annual minima of daily minimum temperatures (TNn) increasing by 2.5°C. Precipitation over the Yangtze River has significantly increased by about 0.2 mm·day⁻¹, while changes over the other two basins were not statistically significant. The uncertainty in the change of precipitation was greater than that of temperature. A selection of simulations from the Fifth and Sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6) were validated against the CN05.1 dataset for the historical period of 1961–2018. Changes in temperature indices were well‐reproduced, but changes in precipitation indices poorly so. CMIP6 models performed better than the CMIP5 models. Both CMIP5 and CMIP6 multi‐model ensembles (MMEs) projected about 1.0–2.0°C warming over China and the three river basins by 2015–2050. Both MMEs projected wetting trends over most parts of China and the three river basins. Both warming and wetting were projected to accelerate with time, particularly warming over the Yellow River basin, and wetting over the Pearl River basin.

To better predict and understand land–atmospheric interactions in the Tibetan Plateau (TP), we used Moderate Resolution Imaging Spectroradiometer (MODIS)-based land-use data and the MODIS-derived green vegetation fraction (GVF) to analyze the variation trend over the TP. The in situ observations from six flux stations (“BJ” (the BJ site of Nagqu Station of Plateau Climate and Environment), “MAWORS” (the Muztagh Ata Westerly Observation and Research Station), “NADORS” (the Ngari Desert Observation and Research Station), “NAMORS” (the Nam Co Monitoring and Research Station for Multisphere Interactions), “QOMS” (the Qomolangma Atmospheric and Environmental Observation and Research Station), and “SETORS” (the Southeast Tibet Observation and Research Station for the Alpine Environment)) at the Chinese TP Scientific Data Center were used to study the surface energy variation characteristics and energy distribution over different underlying surfaces. Finally, we used observation data to verify the applicability of the ERA-5 land reanalysis data to the TP. The results showed that the annual GVF steadily declined from the southeast parts to the northwest parts of the TP, and the vegetation coverage rate was highest from June to September. The sensible heat flux (H), latent heat flux (LE), net surface radiation (Rn), and four-component radiation (solar downward shortwave radiation (Rsd), surface upward shortwave radiation (Rsu), atmospheric downward longwave radiation (Rld), and surface upward longwave radiation (Rlu)) reached their maxima in summer at each station. Rld did not change significantly with time; all other variables increased during the day and decreased at night. The interannual variation in H and LE shows that latent heat exchange was the dominant form of energy transfer in BJ, MAWORS, NAMORS, and SETORS. By contrast, sensible heat exchange was the main form of energy transfer in NADORS and QOMS. The Bowen ratio was generally low in summer, and some sites had a maximum in spring. The surface albedo exhibited a “U” shape, decreasing in spring and summer, and increasing in autumn and winter, and reaching the lowest value at noon. Except for SETORS, ERA-5 Land data and other flux stations had high simulation accuracy and correlation. Regional surface energy changes were mainly observed in the eastern and western parts of the TP, except for the maximum of H in spring; the maximum values of other heat fluxes were concentrated in summer.

The Qinghai–Tibet Plateau (QTP) is a crucial component of the global climate system, influencing the regional and global climate through complex thermal and dynamic mechanisms. The high-altitude region, which is the largest part of the extra-polar cryosphere, encompasses extensive mountain glaciers, permafrost, and seasonally frozen land, making it highly sensitive to global climate change. However, the challenging environmental conditions, such as the harsh terrain and high altitude, coupled with sparse weather station distribution and weak observatory representation, make it difficult to accurately quantify the atmospheric conditions and land–atmosphere coupling systems and their effects on the surrounding areas. To address these challenges, we utilized the Weather Research and Forecasting (WRF) model and a three-dimensional variational (3DVAR) assimilation method to create a high-resolution assimilated dataset (HRAD). The QTP-HRAD, covering the spatial range of 70 to 110°E and 25 to 40°N, was validated using both surface weather station observations and the European Center for Medium-Range Weather Forecasts Reanalysis V5, and can now be utilized for further studies on land–atmosphere interactions, water cycling and radiation energy transfer processes, and extreme weather events in the region.

The atmospheric boundary layer is a key region for human activities and the interaction of various layers and is an important channel for the transportation of momentum, heat, and various substances between the free atmosphere and the surface, which has a significant impact on the development of weather and climate change. During the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) in June 2022, utilizing the comprehensive stereoscopic observation experiment of the “Plateau Low Vortex Network”, this study analyzed the variation characteristics and influencing factors of the atmospheric boundary layer height (ABLH) at three stations with different underlying surface types on the Qinghai–Tibet Plateau (QTP): Qumalai Station (grassland), Southeast Tibet Observation and Research Station for the Alpine Environment (SETORS, forest), and Sieshan Station (cropland). The analysis utilized sounding observation data, microwave radiometer data, and ERA5 reanalysis data. The results revealed that the temperature differences between the sounding observation data and microwave radiometer data were minor at the three stations, with a notable temperature inversion phenomenon observed at Sieshan Station. Regarding water vapor density, the differences between the sounding observation data and microwave radiometer data were relatively small at Sieshan Station. The relative humidity increased with height at Sieshan Station, whereas it increased and then decreased with height at SETORS and Qumalai Station. The ABLH at all sites reached its maximum value around noon, approximately 1500 m, and exhibited mostly convective boundary layer (CBL) characteristics. During the night, the ABLH mostly showed a stable boundary layer (SBL) pattern, with heights around 250 m. In summer, latent heat flux (LE) and sensible heat flux (H) in the eastern plateau were generally lower than those in the western plateau except at 20:00, where they were higher. Vertical velocity (w) in the eastern plateau was greater than in the western plateau. Among Sieshan Station and SETORS, LE, and H had the most significant impact on ABLH, while at Qumalai Station, ABLH was more influenced by surface long-wave radiation (Rlu). These four influencing factors showed a positive correlation with ABLH. The impact of different underlying surface types on ABLH primarily manifests in surface temperature variations, solar radiation intensity, vegetation cover, and terrain. Grasslands typically exhibit a larger range of ABLH variations, while the ABLH in forests and mountainous cropland areas is relatively stable.