High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs’ simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO₂ concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.

Using a decade of ground‐based soil moisture observations acquired from the United States Department of Agriculture's Soil Climate Analysis Network (SCAN), we calculate the mutual information (MI) content between multiple soil moisture variables and near‐future vegetation condition to examine the existence of emergent drought information in vertically integrated (surface to 60 cm) soil moisture observations (θ_{0–60 [cm]}) not present in either superficial soil moisture observations (θ_{5 [cm]}) or a simple low‐pass transformation of θ₅. Results suggest that while θ_0–60 is indeed more valuable than θ₅ for predicting near‐future vegetation anomalies, the enhanced information content in θ_0–60 soil moisture can be effectively duplicated by the low‐pass transformation of θ₅. This implies that, for drought monitoring applications, the shallow vertical penetration depth of microwave‐based θ₅ retrievals does not represent as large a practical limitation as commonly perceived.

The northern terrestrial biomes are being remarkably altered by climate change. Higher springtime temperature induces the earlier greening of vegetation, which may further influence ecosystem functions during the subsequent season. However, the response of summer net ecosystem productivity to spring vegetation greenness and phenology changes has not yet been quantified. To understand the impact of such phenological changes on terrestrial carbon sink of the following season, here we integrate remotely-sensed vegetation data and model simulations of carbon flux with an explainable machine learning approach. We find that the lagged effects of widespread earlier spring greening are increasing the summer ecosystem carbon sink across the northern vegetated areas (30° to 90°N) from 1982 to 2015. In particular, response disparities exist in non-agricultural biomes, and the vegetation with moderate tree coverage is more sensitive to earlier spring greening. Furthermore, modest tree restoration can strengthen the beneficial effects of earlier spring greening. This study improves our understanding of interseasonal vegetation-climate-carbon coupling that drives the key ecological feedback within climate change projections.

Under a warmer climate, the enhancement of dry-wet abrupt alternation (DWAA) risk poses a great challenge for sustainable development. Here, we introduce a novel framework for DWAA detection based on our proposed soil moisture concentration index. By the end of this century, over humid southern China, the shift of soil moisture time series from anomalously wet to anomalously dry pattern, or the other way around, will be more abrupt. In addition, the proposed framework driven by Coupled Model Intercomparison Project Phase 6 simulations projects more widespread DWAA-affected areas over southwestern China, coastal regions of southeastern China, and the lower reaches of the Yangtze River, especially under a high emission scenario. The framework proposed in this study provides an efficient system for DWAA detection and prediction, and the findings of this study provide a reference for upgrading hydraulic infrastructure and mitigating future DWAA events.