Based on the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim reanalysis data from 1979 to 2016, an index for Tibetan Plateau (TP) soil moisture (I_TPSM), which could represent inter‐annual variation in spring soil moisture, is defined after removing the linear trend in soil moisture. The late spring soil moisture variation over the TP and its influences on the plateau summer monsoon are analysed. The results show that the TP soil moisture variation is non‐uniform, and spring soil moisture over the northeastern TP shows an increasing trend, while the western TP (especially the peripheral area) renders a significantly decreasing trend. When the I_TPSM shows positive (negative) anomalies (i.e., spring soil moisture over the northeastern TP is higher (lower), soil moisture over the western TP is lower (higher) than normal), the plateau summer monsoon may be stronger (weaker). Anomalous variations in spring TP soil moisture can eventually affect the plateau summer monsoon by changing atmospheric circulation, diabatic heating, and water vapour transportation. It is found that when the I_TPSM exhibits positive anomalies, diabatic heating over the centre and eastern TP is remarkably enhanced, and warm and humid moisture from the Bay of Bengal and Arabian Sea and cold air from high latitudes merge over the central and eastern TP; coupled with lower‐level convergence and upper‐level divergence, all these contribute to an increase in plateau summer monsoonal precipitation. When the I_TPSM shows negative anomalies, diabatic heating over the centre and eastern TP is weakened, the anomalous cyclone to the south of the TP hinders warm and humid air flows transportation to the TP, and the anomalous anticyclone to the southeast of Lake Baikal weakens cold air to the south; coupled with weakened convergence and upwards motion, these factors all lead to a decrease in plateau summer monsoonal precipitation.

Drylands are the world’s largest biome and dominate the trends and interannual variability of global carbon sinks. Although a ‘greening’ trend of global drylands has been widely reported, large uncertainties remain in attributing its drivers. It is increasingly emphasized that elevated CO₂ has greatly contributed to the vegetation greening over global drylands. Here we quantified the contributions of climate change, elevated CO₂, and land use and land cover change (LULCC) on leaf area index (LAI) over drylands, using a process-based land surface model Noah-MP to investigate the drivers of vegetation change. The state-of-the-art model shows better performance in simulating the interannual variability of satellite-observed LAI over global drylands compared with that of the multi-model ensemble mean LAI from the TRENDY results. The area that LAI changes dominated by climate change (44.03%) is three times greater than that by CO₂ (13.89%), and climate change also contributes most to the global drylands greening trend (55.07%). LULCC shows regional dominance over 13.35% of the global drylands, which is associated with afforestation, woody plant encroachment, and agricultural intensification. Our results imply that the vegetation greening area driven by elevated CO₂ is much limited relative to the overwhelming climatic driving, which should be considered in predictions of trends and interannual variations of global carbon sinks.

The evolution of plateau monsoons is essential to synoptic climatology processes over the Qinghai-Xizang Plateau. Based on ERA-Interim Reanalysis data covering 1979–2014 from the European Centre for Medium-Range Weather Forecasts (ECMWF), we propose a new plateau monsoon index (ZPMI) that can effectively reflect the evolution of monsoons and compare this new index with the existing Plateau Monsoon Indices (PMI), i.e., the Traditional Plateau Monsoon Index (TPMI), the Dynamic Plateau Monsoon Index (DPMI), and the PMI proposed by Qi et al. (QPMI). The results show that the onset and retreat of plateau monsoons determined by the TPMI are approximately 1–2 months earlier than those of the ZPMI and DPMI and that the ZPMI can better reflect seasonal and inter-annual variations in precipitation over the plateau. The plateau summer and winter monsoons have similar inter-annual and inter-decadal variation characteristics and show a rising trend, but the increasing trend of the summer monsoon is more significant. The ZPMI is also capable of effectively reflecting meteorological elements. In stronger plateau summer monsoon years, more (less) precipitation and a higher (lower) air temperature appear over the eastern and central (western) plateau. The ZPMI and soil moisture in April and May are used to explore the influence of soil moisture on plateau monsoons, and a significant correlation is found between the plateau soil moisture in the spring (April–May) and plateau summer monsoons. It is found that when the soil moisture over the central and eastern plateau is higher (lower) than normal (while the soil moisture over the western plateau is lower (higher)), the plateau summer monsoon may be stronger (weaker).

Previous research has primarily focused on assessing seasonal mean or annual extreme climate events, whereas intraseasonal variability in extreme climate has received comparatively little attention, despite its importance for understanding short-term climate dynamics and associated risks. This study evaluates the performance of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing summer maximum temperature (Tmax) variability across China during 1979–2014, with the variability defined as the standard deviation of daily Tmax anomalies for each summer. Results show that most CMIP6 models fail to reproduce the observed north–south gradient of Tmax variability with significant regional biases and limited agreement on temporal trends. The multi-model ensemble (MME) outperforms most individual models in terms of root-mean-square error and spatial correlation, but it still under-represents the observed temporal trends, especially over southeastern and central China. Taylor diagram analysis reveals that EC-Earth3, GISS-E2-1-G, IPSL-CM6A-LR, and the MME perform relatively well in capturing the spatial characteristics of Tmax variability, whereas MIROC6 shows the poorest performance. These findings highlight the persistent limitations in simulating intraseasonal Tmax variability and underscore the need for improved model representations of regional climate dynamics over China.