Extreme precipitation within the context of global climate change has dramatic impacts on terrestrial carbon sequestration. While extensive research has focused on the adverse impacts of droughts on terrestrial carbon sinks, the effects of extreme precipitation events remain underexplored. Here we investigated the carbon sink dynamics induced by a record-breaking heavy precipitation event over the Yangtze River Valley (YRV) in JuneJuly (JJ) 2020, using OCO-2 v10 MIP posterior data and simulations from two terrestrial biosphere models (VEGAS and LPJwsl). Our results show that extreme precipitation in JJ caused a significant decline in net biome productivity (NBP), with reductions of approximately - 16.75 Tg C by OCO-2 v10 MIP, -23.50 Tg C by VEGAS, and - 16.88 Tg C by LPJwsl, predominantly driven by substantial decreases in gross primary production (GPP). Following the cessation of precipitation in August, negative NBP anomalies persisted due to stronger total ecosystem respiration (TER*), but rapid recovery was observed, with recovery rates of 55.40 %, 83.58 %, and 86.85 %, respectively, driven by a resurgence in GPP. Extreme precipitation also triggered significant variations in temperature, soil moisture, surface downward solar radiation (RAD), and vapor pressure deficit (VPD), all of which influenced NBP. Attribution analysis revealed reduced RAD as the primary factor behind negative NBP anomalies during JJ, with contributions of approximately - 19.36 Tg C in VEGAS and - 8.54 Tg C in LPJwsl. In August, VEGAS emphasized negative legacy effects from JJ, while LPJwsl pointed to the suppressive role of high temperatures. Furthermore, both models consistently underscored the pivotal role of RAD in carbon sink recovery. Considering the increasing frequency and intensity of heavy precipitation under global warming, our study emphasized the negative effects of extreme precipitation on the terrestrial carbon sequestration, providing the further understanding on interactions of extreme climatic events and terrestrial ecosystems.

Abstract. Compound dry-hot events are intensifying under climate change and pose growing risks to agricultural production. From April to June 2024, the North China Plain (NCP) experienced an extreme compound dry-hot event. Using satellite-based normalized difference vegetation index (NDVI), gross primary productivity (GPP), and crop yield statistics, this study quantified crop growth responses and identified the dominant climatic drivers during this event. The climate anomaly was characterized by pronounced warming in April and June, a continuous decline in precipitation and soil water from April onward, and a record-high vapor pressure deficit (VPD) in June, forming a persistent dry-hot stress. NDVI and GPP increased markedly in April and remained slightly positive in May, but both collapsed to their lowest levels since 2000 in June. Consistent with these vegetation signals, provincial yield statistics and experimental plot observations showed increased winter wheat yields but reduced summer maize yields. Sensitivity and contribution analyses revealed distinct phenology-modulated mechanisms: in April, elevated temperatures and vegetation carryover effects comparably enhanced vegetation activity in winter-wheat-dominated croplands; in May, vegetation dynamics were controlled almost entirely by the previous-month carryover effect, reflecting the growing influence of accumulated vegetation state; and in June, as winter wheat reached maturity and newly sown maize entered early establishment, VPD emerged as the primary limiting factor, strongly suppressing photosynthetic activity and seedling establishment. These findings demonstrate how phenological transitions modulate crop vulnerability to compound dry-hot events and provide useful insights for agricultural early warning, crop management, and climate adaptation strategies in the NCP.

2024 is the hottest year on record, accompanied by extreme precipitation, droughts and fires. The global atmospheric CO2 growth rate in 2024 reached a historic high of 3.73 ppm yr-1, significantly surpassing the previous record set during the 2015/16 El Niño event. Here, we investigate the causes and underlying mechanisms of this record-high growth rate by combining satellite-based atmospheric inversions and estimates of gross primary production and fire emissions. We find that the record-high CO2 growth rate is due to large reductions in the land CO2 sink. This is dominated by a dramatic increase in total ecosystem respiration, which occurred primarily in grass and shrub lands, owing to compound hot-wet climatic conditions in 2024. Given the projected increase in the frequency and intensity of compound pluvial-hot extremes under warming, changes in ecosystem respiration will become more drastic and cause positive feedback to climate warming.

During the July-September (JAS) of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). It caused an unprecedented decline in vegetation photosynthesis, however, its impact on the regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using the Global Carbon Assimilation System (GCAS v2) by assimilating OCO-2 XCO2 retrievals. Our results indicate that, relative to 2015-2021, the MLYR region experienced a 45.8 TgC reduction in land sink during JAS, consistent with the TRENDYv13 simulations. Combining our inverse results with satellite proxies for GPP, we find that an unusually wet spring in 2022 boosted vegetation growth in the MLYR, increasing gross primary productivity (GPP) by 46.1 TgC and strengthening the land sink by 24.0 TgC, thereby substantially offsetting the carbon sink reductions observed during JAS. Outside the MLYR region in southern China, annual land sink increased by 49.9 TgC in remaining areas (RAS), also greatly mitigating the impact of the DH2022 on the regional carbon balance. Overall, the annual land sink in MLYR decreased by only 7.1 TgC, whereas in southern China, it increased by 42.8 TgC. During JAS, the decreased land sink in MLYR was primarily driven by a decline in GPP in forests and grass/shrub, coupled with an increase in total ecosystem respiration in croplands. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing our understanding of the impacts of climate extremes on the regional carbon cycle.

This study investigates the interannual variability in the intensity of the quasi-biweekly oscillation (QBWOI) of the atmospheric heat source over the Tibetan Plateau (TP) and its linkage with summer precipitation anomalies in China. The results reveal distinct propagation characteristics of quasi-biweekly signals between strong and weak QBWOI years. During strong QBWOI years, most quasi-biweekly oscillations propagate northward from low latitudes toward the TP, accompanied by enhanced moisture transport that converges over the southern TP. In contrast, during weak QBWOI years, outward propagation from the TP becomes more frequent, leading to regional moisture export. Corresponding to interannual variability in TP QBWOI, precipitation across China exhibits an asymmetric pattern: strong QBWOI years feature increased precipitation south of the middle and lower reaches of the Yangtze River, whereas weak years correspond to suppressed precipitation anomalies over the southern TP. The associated large-scale circulation patterns indicate that an East Asia-Pacific-like wave train contributes to the anticyclonic anomaly in the tropical Northwest Pacific during strong years, while two southeastward wave trains from Northern Europe and the Arctic in the mid-high latitudes, driven by the Arctic Oscillation, plays a more important role in forming a barotropic positive height anomaly over the northeastern TP in weak years. Overall, the TP QBWOI reflects systematic atmospheric configurations that are closely linked to interannual summer precipitation variability across China.

Hydroclimatic extremes are critical regulators of terrestrial carbon sink dynamics, yet their representation in terrestrial biosphere models remains highly uncertain. Here, we assessed uncertainties in Trends in Net Land-Atmosphere Exchange (TRENDY) v12 model simulations of carbon sink responses to hydroclimatic extremes during 1980-2022 by systematically comparing model outputs across regions, event types, and biomes. Site-level evaluations reveal that the multi-model ensemble mean correctly captures the sign of net biome productivity (NBP) anomalies at approximately 60% of stations; however, while the multi-model ensemble mean generally replicates NBP variations during dry events, its performance degrades during wet events. Spatially, most regions act as anomalous carbon sinks during wet extremes, a pattern that largely reverses during dry events. Despite these general trends, substantial inter-model heterogeneity persists. Inter-model uncertainties are more pronounced under dry events between 30 degrees S and 30 degrees N, while other latitudes exhibit comparable or even greater spreads under wet events. Specifically, inter-model spread is more sensitive to wet anomalies in arid and semi-arid regions, but to drought-induced stress in semi-humid and humid regions. Across biomes, uncertainties are greater for grasslands, savannas, and shrublands during wet events, shifting to forests and croplands during dry events. Finally, we demonstrate that the divergent NBP responses primarily originate from uncertainties in simulating gross primary production. Our findings highlight the persistent challenges TRENDY models face in capturing ecosystem responses to hydroclimatic extremes, underscoring the urgent need to improve simulation fidelity in a rapidly changing climate.

Recent extreme heatwaves in eastern China have caused escalating socio-economic and environmental vulnerabilities. This study identifies distinct sub-seasonal variability in summer heatwaves, with the North China Plain (NCP) prominently affected in June and the Yangtze River Valley (YRV) during July–August (JA). The Tibetan Plateau atmospheric heat source (TP AHS) exhibits corresponding spatial differences, displaying significant interannual co-variability with regional heatwave features. During June, an intensified TP AHS is accompanied by a southward displacement of the upper-level westerly jet stream, driving differential thermal advection and deep subsidence over the NCP. Spatially overlapping with this aloft downwelling, the westward expansion of the western Pacific subtropical high (WPSH) aligns with a positive horizontal temperature advection center over the NCP, closely tied to an anomalous mid-to-lower tropospheric cyclonic circulation along the northern flank of the WPSH. During JA, the enhanced TP AHS corresponds to a northward jet stream shift and a northwestward relocation of the South Asian High (SAH), establishing anomalous upper-level convergence over the YRV. These multi-scale circulation configurations, together with a V-shaped isentropic downglide structure radiating from the TP, form a coherent dynamic framework driving persistent subsidence over the YRV. Additionally, these dynamically aligned subsidence zones feature prominent cloud reduction that enhances surface downward solar radiation and sensible heat flux. Through a land–atmosphere coupling loop, these localized diabatic feedbacks structurally match the enhanced intensity and persistence of regional extreme heatwaves. These findings demonstrate that the TP AHS exhibits a month-dependent configuration structurally coupled with sub-seasonal heatwaves across eastern China, co-varying with 35–51% of the extreme heatwave days. This provides process-level insights into the physical linkages between upstream TP thermal anomalies and downstream thermodynamic extremes.

Bottom-up coal mine methane (CMM) inventories rely on static or empirically derived emission factors (EFs), and therefore mine-level emissions are poorly constrained, limiting the use of these inventories for implementing detailed mitigation strategies. Here, we compiled 1418 satellite-detected methane plumes (2019 – 2025) and attributed them to 159 active underground coal mines in Shanxi province, China. We further derived observed mine-level EFs, calculated as mine-level emission rates divided by production data. We then compared these observed EFs with those from the State Administration of Coal Mine Safety (SACMS) and Global Coal Mine Tracker (GCMT), and developed a production-capacity-stratified bootstrap framework to upscale emissions from high-gas and outburst coal mines. Observed EFs were highly heterogeneous, right-skewed, temporally variable and negatively correlated with production capacity. Inventory comparisons revealed distinct biases: SACMS reproduced the overall EF magnitude but systematically underestimated EFs for small-capacity coal mines (production capacity <1.2 Mt yr ^−1 ), whereas GCMT overestimated EFs for medium- (1.2⩽ production capacity <3.0 Mt yr ^−1 ) and large-capacity coal mines (production capacity ⩾3.0 Mt yr ^−1 ). Using the production-capacity-stratified bootstrap upscaling framework, we estimated 2023 CMM emissions from high-gas and outburst coal mines in Shanxi to be 7.0 [5.6 – 8.9] Mt yr ^−1 . Total provincial CMM emissions were estimated at 11.2 [9.3 – 13.6] Mt yr ^−1 . These findings show that satellite-observed plumes can constrain mine-level EFs, reveal inventory biases, and support observation-based provincial CMM estimation.

Abstract. Lightning is a primary driver of severe convective hazards and wildfire ignitions, yet long-term, high-resolution gridded records have remained scarce due to the limited temporal coverage of ground-based networks and the sampling constraints of satellite observations. Here, we presented a new global 0.25° × 0.25° monthly land lightning stroke-density dataset spanning 1979–2025. To ensure robustness, we developed a ridge regression stacking ensemble that integrated four complementary machine learning architectures: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Deep Neural Network (DNN). The ensemble achieved superior performance over each single model (test R² = 0.6895, RMSE = 0.0108, MAE = 0.0030), indicating that model blending effectively enhanced predictive stability. Individual validations confirmed high spatial fidelity, as the ensemble successfully reproduced the observed large-scale spatial distribution and major tropical–subtropical continental lightning hotspots. Independent comparisons with the LIS/OTD gridded lightning climatology (±38°) further demonstrated strong spatiotemporal consistency, particularly in reproducing interannual variability. Our analysis revealed pronounced regional heterogeneity in multi-decadal trends: significant decreases were concentrated across several tropical convective centers, while localized increases emerged in specific mid-latitude regions. Attribution based on SHapley Additive exPlanations (SHAP) elucidated that these patterns were primarily governed by the coupling of thermodynamic instability (CAPE × TP), moisture availability, and ice-phase hydrometeor conditions. This dataset provided a physically constrained and spatially detailed basis for studying long-term lightning dynamics, offering practical inputs for natural-ignition modeling, lightning-produced NOx estimation, and the evaluation of lightning parameterizations in climate and Earth system models. The datasets of the 1979–2025 Global Land Lightning Density Reconstruction Version 1 (GLLDR v1) are publicly available at the Zenodo via the following DOI: https://doi.org/10.5281/zenodo.19722380 (Zheng et al., 2026a).

The summer of 2022 was marked by unprecedented heatwaves and droughts across Europe and the Yangtze River Basin (YRB) in China, triggering record-breaking negative anomalies in gross primary productivity (GPP) since 2000. To elucidate the drivers of these shifts, we employed a machine-learning-based factorial experimental design using FluxSat GPP data to quantify the contributions of concurrent climatic drivers and short-term legacy effects—specifically biotic vegetation growth carryover (VGC) and abiotic lagged climatic effects (LCE). Our results demonstrate that legacy effects are the primary drivers of GPP fluctuations, with the preceding month exerting the strongest influence. Attribution analysis further reveals that during the peak of these compound hot-dry events, vapor pressure deficit (VPD) was the dominant driver of GPP anomalies. However, VGC from the previous month subsequently emerged as the leading factor, with its relative contribution intensifying as the events progressed.

The response of net forest carbon uptake to warm extremes remains elusive. The year 2023 was at the time ‘the hottest year on record’ globally, with Canada’s forests experiencing warm anomalies of above 2 °C and unprecedented drought and wildfires, providing a unique case to examine the response of boreal forest net carbon uptake to climate extremes. Here we combine satellite-based atmospheric CO2 flux inversions with ground-based in situ observations of CO2 fluxes and concentrations to investigate Canada’s forest net carbon uptake and its underlying mechanisms in 2023. We find that, compared with 2015–2022, Canada’s forest net carbon uptake was enhanced by 0.28 ± 0.23 PgC, offsetting 38–48

This study investigates intraseasonal oscillation intensity (ISOI) variations of the atmospheric heat source over the Tibetan Plateau (TP) during 1979-2020 and their relationship with local summer precipitation. Our analysis reveals that 10-20-day oscillation predominates over TP, followed by 20-30-day oscillation. ISOIs of both periodicities exhibit signifi- cant interannual and decadal variations, with notable weakening trends, particularly over the southern TP. Further analysis indicates a strong relationship between the ISOI and the north-south reversed variations in precipitation across the TP. Mechanically, a weakening of the 10-20-day ISOI correlates with decreased water vapor transport from the Bay of Bengal and the Arabian Sea into the southern TP. This reduction, combined with upper-lower circulation anomalies, leads to a downdraft over the southern TP and reduced precipitation here. Conversely, when the 20-30-day ISOI weakens, water vapor transport from the Arabian Sea into the southern TP diminishes while accumulating over the northeastern TP. Together, an anticlockwise meridional circulation occurs between the northern and southern TPs, causing decreased precipitation in the southern TP and increased precipitation in the northern TP. Dynamic diagnostics further identifies that negative temperature advection and changes in nonadiabatic heating dQ/dt are crucial for the downdraft over the southern TP when the 10-20-day ISOI weakens. In contrast, the weakening of the 20-30-day ISOI involves horizontal relative vorticity and temperature advection in midlevel and upper level, along with dQ/dt in lower levels, which contributes to the downdraft over the southern TP. Meanwhile, the updraft over the northern TP is predominantly linked to horizontal relative vorticity. SIGNIFICANCE STATEMENT: Intraseasonal oscillation intensity (ISOI) variations can influence climate changes across multiple scales. This study focuses on recent variations in ISOI over the Tibetan Plateau (TP), an area where such effects remain poorly understood. We explore the relationship between ISOI and the north-south reversed variations in summer mean precipitation over the TP, revealing the associated potential physical mechanisms and dynamical processes. Our findings highlight the significant connection between ISOI and climate changes.

Fire CO 2 emissions are a critical component of the global carbon cycle, yet their estimates remain highly uncertain. This study introduces a satellite‐constrained inversion framework that jointly optimizes fire emissions and net ecosystem exchange using OCO‐2 XCO 2 retrievals. An observing system simulation experiment demonstrates the approach's capability to improve emission estimates, especially in regions where fires occur during the non‐growing season. Applied to Africa, the inversion yields fire emissions of 1.18 ± 0.22 PgC yr −1 for 2015–2016––about 20% higher than GFED4s and GFAS averages. Regionally, emissions were underestimated in northern Africa (∼0.25 PgC yr −1 ) due to missing burned area and overestimated in southern Africa (∼0.05 PgC yr −1 ) due to inflated fuel assumptions. The inversion reduces inter‐inventory discrepancies by 88% and reveals pronounced landscape‐dependent biases. These findings highlight the potential of XCO 2 ‐based joint inversions to enhance regional emission estimates and improve representations of fire–carbon–climate feedbacks in Earth system models.

Ecosystems modulate Earth’s climate through the exchange of carbon and water fluxes. However, long-term trends in these terrestrial fluxes remain unclear due to the lack of continuous measurements on the global scale. This study combined flux data from 197 eddy covariance sites with satellite-retrieved solar-induced chlorophyll fluorescence (SIF) to investigate spatiotemporal variations in gross primary productivity (GPP), evapotranspiration (ET), and their coupling via water use efficiency (WUE) from 2001 to 2020. We developed six global GPP and ET products at 0.05° spatial and 8-day temporal resolution, using two machine learning models and three SIF products, which integrate vegetation physiological parameters with data-driven approaches. These datasets provided mean estimates of 128 ± 2.3 Pg C yr−1 for GPP, 522 ± 58.2 mm yr−1 for ET, and 1.8 ± 0.21 g C kg−1 H2O yr−1 for WUE, with upward trends of 0.22 ± 0.04 Pg C yr−2 in GPP, 0.64 ± 0.14 mm yr−2 in ET, and 0.0019 ± 0.0005 g C kg−1 H2O yr−2 in WUE over the past two decades. These high-resolution datasets are valuable for exploring terrestrial carbon and water responses to climate change, as well as for benchmarking terrestrial biosphere models.

The accurate quantification of anthropogenic carbon dioxide (CO2) emissions in urban areas is hindered by high uncertainties in emission inventories. We assessed the spatial distributions of three anthropogenic CO2 emission inventories in Shanghai, China—MEIC (0.25° × 0.25°), ODIAC (1 km × 1 km), and a local inventory (LOCAL) (4 km × 4 km)—and compared simulated CO2 column concentrations (XCO2) from WRF-CMAQ against OCO-3 satellite Snapshot Mode XCO2 observations. Emissions differ by up to a factor of 2.6 among the inventories. ODIAC shows the highest emissions, particularly in densely populated areas, reaching 4.6 and 8.5 times for MEIC and LOCAL in the central area, respectively. Emission hotspots of ODIAC and MEIC are the city center, while those of LOCAL are point sources. Overall, by comparing the simulated XCO2 values driven by three emission inventories and the WRF-CMAQ model with OCO-3 satellite XCO2 observations, LOCAL demonstrates the highest accuracy with slight underestimation, whereas ODIAC overestimates the most. Regionally, ODIAC performs better in densely populated areas but overestimates by around 0.22 kt/d/km2 in relatively sparsely populated districts. LOCAL underestimates by 0.39 kt/d/km2 in the center area but is relatively accurate near point sources. Moreover, MEIC’s coarse resolution causes substantial regional errors. These findings provide critical insights into spatial variability and precision errors in emission inventories, which are essential for improving urban carbon inversion.

Microplastic (MP) pollution has become a global environmental problem with profound impacts on aquatic ecosystems. Although the topic of MPs has attracted high attention, the sources, transport pathway, and removal of MPs in river networks is still unclear. Here, we conducted a field survey across the Pearl River Basin (PRB) (> 4.5 × 105 km2) and collected the water samples to characterize the spatial distribution of MPs using a Laser Direct Infrared (LDIR) chemical imaging system. The MPs were detected in all samples with an average abundance of 1092.86 items/L, in which polyamide (PA), polyurethane (PU), and polyvinyl chloride (PVC) are the main polymer types. Population and surface runoff were identified as major factors influencing the concentrations of MPs. The Partial Least Squares Structural Equation Modeling (PLS-PM) analysis revealed that precipitation-induced surface runoff is a major pathway for MPs transferring from terrestrial environment to river networks. River hydraulic dynamics were found to have considerable influence on the selective removal of MPs from water column in the river channel. The smooth state (Froude number, Fr <0.23) promotes while the rough state (Fr > 0.23) inhibits the deposition of MPs from water column to sediments. In particular, the smooth state facilitates the deposition of large-sized and high-density MPs from the water column to sediments. The deposition processes in river channel cause considerable fractionation of polymer types and size of riverine MPs. This study provides the first-hand MP pollution status in the networks of the PRB and provide insights into sources, spatial distribution characteristics, and transmission mechanism of MPs in river networks, which would provide theoretical bases and experimental reference for river water quality management and risk control of MPs for governor, stakeholders, and policy makers.

Elevated atmospheric carbon dioxide (CO2) concentrations have caused global climate change such as global warming and more frequent climate extremes. Countries worldwide have proposed carbon neutrality strategies to curb the rising CO2 concentrations. To investigate the impact of China’s carbon neutrality goal on atmospheric CO2 concentrations, we conducted a series of ideal simulations from 2015 to 2019 using a global 3D chemistry transport model, Goddard Earth Observing System Chemistry (GEOS-Chem). Compared with the column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) from Orbiting Carbon Observatory-2 (OCO-2) and surface CO2 measurements in ObsPack, we find that GEOS-Chem effectively reproduces the spatiotemporal variability of CO2. The model exhibits a root mean square error (RMSE) of 1.51 ppm (R2=0.89) for OCO-2 XCO2 in China and 2.65 ppm (R2=0.75) for surface CO2 concentrations at the WLG station. Further, compared to 2.83 ppm yr−1 in the control experiment, we suggest that net-zero CO2 emissions in China decelerate the increasing trends of XCO2 to 1.81 ppm yr−1, making a decrease of approximately 35.89

Fire CO2 emissions are a critical component of the global carbon cycle, yet their estimates remain highly uncertain. This study introduces a satellite-constrained inversion framework that jointly optimizes fire emissions and net ecosystem exchange using OCO-2 XCO2 retrievals. An observing system simulation experiment demonstrates the approach's capability to improve emission estimates, especially in regions where fires occur during the non-growing season. Applied to Africa, the inversion yields fire emissions of 1.18 +/- 0.22 PgC yr-1 for 2015-2016--about 20% higher than GFED4s and GFAS averages. Regionally, emissions were underestimated in northern Africa (similar to 0.25 PgC yr-1) due to missing burned area and overestimated in southern Africa (similar to 0.05 PgC yr-1) due to inflated fuel assumptions. The inversion reduces inter-inventory discrepancies by 88% and reveals pronounced landscape-dependent biases. These findings highlight the potential of XCO2-based joint inversions to enhance regional emission estimates and improve representations of fire-carbon-climate feedbacks in Earth system models.