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.

Abstract Accurate quantification of interannual variations in China's fossil fuel CO2 (FFCO2) emissions is critical for climate policy evaluation. However, bottom‐up inventories exhibit growing discrepancies. Here, we estimate China's 2015–2023 FFCO2 emissions using the Regional multi‐Air Pollutant Assimilation System, integrating in situ observations of co‐emitted NO2 and grid‐specific CO2‐to‐NOx ratios. National FFCO2 emissions averaged 11.5 ± 1.5 PgCO2 yr−1, with over 45% from North and East China. Emission intensity exhibited a southeast‐to‐northwest increasing gradient, aligning with economic development patterns. Interannually, emissions rose notably during 2015–2017, driven by North and Central China, then stabilized during 2018–2023 amid policy interventions and COVID‐19 disruptions, contrasting with the sustained growth reported in mainstream bottom‐up inventories. Total annual growth averaged 189.6 TgCO2 yr−1 (1.6%), while emission intensity fell 5.6% annually, with Southwest China experiencing a decline in both metrics. This observation‐constrained data set clarifies spatiotemporal dynamics and policy impacts, supporting targeted strategies for China's carbon goals.

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).

Abstract. Accurately quantifying anthropogenic CO2 emissions is essential for evaluating carbon budget and mitigation strategies. However, traditional "bottom-up" emission inventories suffer from substantial uncertainties and update time lags, urgently requiring top-down constraints from atmospheric observations while accounting for confounding terrestrial biogenic interferences. In this study, we extended RegGCAS, a regional carbon assimilation system based on the WRF-CMAQ atmospheric chemical transport model and the Ensemble Kalman Filter algorithm. By assimilating column-averaged dry-air CO2 mole fractions (XCO2) from OCO-2/3 satellite observations, we inverted anthropogenic CO2 emissions over mainland China during winter 2022–2023. The results revealed that the total national anthropogenic CO2 emissions amounted to 2808.3 ± 157.0 Tg, 16.1 % higher than the MEIC inventory. For key emission regions, emissions increased by 13.1 % in the Beijing-Tianjin-Hebei region, whereas they decreased by 10.4 % in the Yangtze River Delta. The system captured distinct urban-suburban emission adjustment differences in key regions, with reductions in city centers and increases in surrounding areas. It also reflected short-term emission fluctuations related to anthropogenic activity changes, such as the Spring Festival work stoppages. Evaluation demonstrates that assimilation effectively reduces prior emission errors by 68.0 %. Validation shows that posterior simulation RMSE decrease by 5.8 % against the assimilated OCO-2/3 XCO2, and by 15.3 %, 7.7 %, and 25.2 % against independent TCCON, ObsPack, and urban site observations, respectively, confirming the enhanced accuracy of the posterior emission estimates. This study provides a reliable inversion framework for tracking regional carbon dynamics and refining bottom-up emission inventories.

Satellite XCO2 retrievals have been widely used in estimating fossil fuel carbon (FFC) emissions at point and urban scales. However, at the regional scale, it remains a significant challenge. Furthermore, current global and regional atmospheric inversions often overlook the uncertainties associated with FFC emissions. To meet the needs of the global carbon stocktake, we developed an inversion method based on Bayesian statistical theory and OCO-2 satellite XCO2 observations to optimize FFC emissions alongside terrestrial ecosystem carbon fluxes (NEE). The methodology’s core is to distinguish the contributions of NEE and FFC to the observed concentrations using their different spatial distributions. We designed an observing system simulation experiment to invert the 2016 FFC emissions. The results showed that posterior FFC emissions were significantly optimized during the non-growing seasons in the regions with high emissions, with the optimization effect diminishing as emissions shrank. Average FFC emissions uncertainty reductions are in the range of 13–82% in the non-growing season for the eight largest emitting regions globally. By assuming the same uncertainty reduction for FFC emissions in both the growing and non-growing seasons, we can optimize annual emissions for high-emission areas. We believe this study provides a new idea for the inversion of FFC emissions at the regional scale, which is important for achieving the goal of carbon neutrality.

Nitrogen oxides (NOx = NO + NO2) are critical atmospheric pollutants with significant implications for human health and are key precursors of ozone and nitrate aerosols. Anthropogenic emissions primarily from sectors such as transportation, industry, and fossil fuel combustion are the main sources of NOx. The temperate regions of the Northern Hemisphere, which host most industrialized countries and densely populated areas, account for 60~70% of global anthropogenic NOx emissions. As the harmful effects of air pollution gain global attention, nations have implemented various clean energy policies to address these threats. Effective monitoring of anthropogenic NOx emissions and control policies relies on accurate, long-term emission inventories. However, existing “bottom-up” inventories suffer from delays in data compilation, making it difficult to timely and accurately monitor the spatiotemporal variations of NOx emissions. This study presents an effective top-down inversion framework using TROPOMI satellite NO2 observations combined with the GEOS-Chem atmospheric chemical transport model to assess recent NOx emissions. The framework employs a mass balance principle and a two-step inversion approach, extending anthropogenic NOx emissions in the Northern Hemisphere into 2022 and optimizing emissions from 2019 to 2022 for the temperate regions. Our results show a 1.68% decrease in NOx emissions in 2020, followed by a 5.72% rebound in 2021. The recovery in China was faster than in other regions, surpassing 2019 levels by July 2020. In 2022, emissions declined across all regions, driven primarily by the Omicron variant, energy shortages, and clean energy policies. By integrating satellite observations, atmospheric modeling, and emission inversion techniques, our study contributes to the growing body of knowledge on how emissions evolve in response to global disruptions.

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.

The outbreak of the Russia-Ukraine war in 2022 brought a huge impact on the Ukrainian society. We utilized TROPOMI nitrogen dioxide (NO2) observations to constrain the Emissions Database for Global Atmospheric Research (EDGAR) inventory and inverted daily anthropogenic nitrogen oxides (NOx) emissions in Ukraine from 2019 to 2023. Our results reveal a 15 % reduction in NOx emissions during the 2022 war and an 8 % reduction in 2023, both substantially exceeding the decrease caused by the COVID-19 pandemic. Emission anomalies closely tracked the timing and location of major military actions, highlighting the sensitivity of NOx emissions to conflict-related disruptions. Regionally, Eastern Ukraine experienced larger reductions in NOx emissions in both 2022 and 2023 by 29 % and 17 %, respectively, due to direct damage from frontline military operations. In contrast, Western Ukraine experienced a relatively modest emission reduction of only 8 % in 2022 with emissions increasing in some regions. In 2023, the emissions increased in most western regions. After the outbreak of the war, the industrial sector experienced the largest reductions in NOx emissions, with decreases of 34 % and 24 % in 2022 and 2023, respectively, followed by the residential sector, which declined by 23 % and 18 % respectively. War activities also emitted large amounts of NOx, and such emissions partially offset the emission reduction caused by the impact of war on socio-economic. By filtering out high-frequency emission fluctuations induced by wartime activities through Locally Estimated Scatterplot Smoothing, our results indicate that war-related emissions may have offset approximately 8 % and 10 % of the anthropogenic NOx reductions in 2022 and 2023, respectively. After removing the war-related emissions, the inverted emission declines exhibit strong agreement with bottom-up emission inventories and reported economic performance metrics. These findings provide new insights into the environmental and socio-economic impacts of armed conflict.

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.

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.

Methane (CH4), the second most important anthropogenic greenhouse gas, significantly impacts global warming. As the world's largest anthropogenic CH4 emitter, China faces challenges in accurately estimating its emissions. Top-down methods often suffer from coarse resolution, limited data constraints, and result discrepancies. Here, we developed the Regional Methane Assimilation System (RegGCAS-CH4) based on the WRF-CMAQ model and the EnKF algorithm. By assimilating extensive TROPOMI column-averaged dry CH4 mixing ratio (XCH4) retrievals, we conducted high-resolution nested inversions to quantify daily CH4 emissions across China, with a focus on Shanxi Province in 2022. Nationally, posterior CH4 emissions were 45.1 ± 3.8 TgCH4 yr−1, 36.5 % lower than the EDGAR estimates, with the largest reductions in the coal and waste sectors. In North China, emissions decreased most significantly, mainly attributed to the coal and enteric fermentation sectors. Posterior emissions in coal-reliant Shanxi Province decreased by 46.3 %. Sporadic emission increases were detected in major coal-producing cities but were missed by the coarse-resolution inversion. Monthly emissions exhibited a winter-low, summer-high pattern, with the rice cultivation and waste sectors showing higher seasonal increases than those in EDGAR. The inversion significantly improved XCH4 and surface CH4 concentration simulations, reducing emission uncertainty. Compared to other bottom-up/top-down estimates, our results were the lowest, primarily because the high-resolution inversion better captured local emission hotspots. Sensitivity tests underscored the importance of nested inversions in reducing the influence of boundary condition uncertainties on emission estimates. This study provides robust CH4 emission estimates for China, crucial for understanding the CH4 budget and informing climate mitigation strategies.

Abstract. The outbreak of the Russia–Ukraine war in 2022 brought a huge impact on the Ukrainian economic production. To quantify this effect, we invert the anthropogenic Nitrogen oxides (NOx) emissions in Ukraine from 2019 to 2022, a key indicator of human activities, to reflect the disruption of activities in different economic sectors due to war. We found a 28 % decline in NOx emissions during the war, if compared with the base year, which significantly exceeded the decrease caused by the 2020 COVID-19 pandemic. Eastern Ukraine experienced a 34 % decrease in NOx emissions, whereas the other regions experienced a decrease of 24 %. The destruction of infrastructure and energy shortages severely impact the sustainable development of such social activities as industry, housing and transportation in Ukraine. These findings highlight the severe disruption of socio-economic activities due to the war, offering crucial insights into the broader implications of war on environmental and economic stability.

An unprecedented heatwave hit the Yangtze River Basin (YRB) in August 2022. We analyzed changes of anthropogenic CO2 2 emissions in 8 megacities over lower-middle reaches of the YRB, using a near-real-time gridded daily CO2 2 emissions dataset. We suggest that the predominant sources of CO2 2 emissions in these 8 megacities are from the power and industrial sectors. In comparison to the average emissions for August in 2020 and 2021, the heatwave event led to a total increase in power sector emissions of approximately 2.70 Mt CO2, 2 , potentially due to the increase in urban cooling demand. Suzhou experienced the largest increase, with a rise of 1.12 Mt CO2 2 (12.88 %). Importantly, we observed that changes in daily power emissions exhibited strong linear relationships with temperatures during the heatwave, albeit varying sensitivities across different megacities (with an average of 0.0076 +/- 0.0075 Mt d- 1 degrees C-1). C- 1 ). Conversely, we find that industrial emissions decreased by a total of 8.45 Mt CO2, 2 , with Shanghai seeing the largest decrease of 4.71 Mt CO2, 2 , while Hangzhou experienced the largest relative decrease (-21.22 %). It is noteworthy that the majority of megacities rebounded in industrial emissions following the conclusion of the heatwave. We convincingly suggest a tight linkage between the reductions in industrial emissions and China's policy to ensure household power supply. Overall, the reduction in industrial emissions offset the increase in power sector emissions, resulting in weaker emissions for majority of megacities during the heatwave. Despite remaining uncertainties in the emissions data, our study may offer valuable insights into the complexities of anthropogenic CO2 2 emissions in megacities amidst frequent summer heatwaves intensified by greenhouse warming.

Anthropogenic nitrogen oxide (NO _x ) emissions are closely associated with human activities. In recent years, global human activity patterns have changed significantly owing to the COVID‐19 epidemic and international energy crisis. However, their effects on NO _x emissions are not yet fully understood. In this study, we developed a two-step inversion framework using NO _2 observations from the TROPOMI satellite and the GEOS-Chem global atmospheric chemical transport model, and inferred global anthropogenic NO _x emissions from 2019 to 2022, focusing on China, the United States (U.S.), and Europe. Our results indicated an 1.68% reduction in NO _x emissions in 2020 and a 5.72% rebound in 2021 across all regions. China rebounded faster than the others, surpassing its 2019 levels by July 2020. In 2022, emissions declined in all regions, driven mainly by the Omicron variant, energy shortages, and clean energy policies. Our findings provide valuable insights for the development of effective future emission management strategies.

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO(2)yr(-1), with an uncertainty (1 sigma) of 1.5 PgCO(2)yr(-1) that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

Gross primary production (GPP), a crucial component in the terrestrial carbon cycle, is strongly influenced by large-scale circulation patterns. This study explores the influence of the El Ni & ntilde;o-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on China's GPP, utilizing long-term GPP data generated by the Boreal Ecosystem Productivity Simulator (BEPS). Partial correlation coefficients between GPP and ENSO reveal substantial negative associations in most parts of western and northern China during the September-October-November (SON) period of ENSO development. These correlations shift to strongly positive over southern China in December-January-February (DJF) and then weaken in March-April-May (MAM) in the following year, eventually turning generally negative over southwestern and northeastern China in June-July-August (JJA). In contrast, the relationship between GPP and IOD basically exhibits opposite seasonal patterns. Composite analysis further confirms these seasonal GPP anomalous patterns. Mechanistically, these variations are predominantly controlled by soil moisture during ENSO events (except MAM) and by temperature during IOD events (except SON). Quantitatively, China's annual GPP demonstrates modest positive anomalies in La Ni & ntilde;a and negative IOD years, in contrast to minor negative anomalies in El Ni & ntilde;o and positive IOD years. This outcome is due to counterbalancing effects, with significantly larger GPP anomalies occurring in DJF and JJA. Additionally, the relative changes in total GPP anomalies at the provincial scale display an east-west pattern in annual variation, while the influence of IOD events on GPP presents an opposing north-south pattern. We believe that this study can significantly enhance our understanding of specific processes by which large-scale circulation influences climate conditions and, in turn, affects China's GPP.

Urban areas are the largest contributors to global fossil fuel carbon emissions, and controlling urban carbon emissions is critical to addressing climate change and reducing greenhouse gas emissions. However, estimates of urban emissions remain large uncertainties, making it difficult to accurately understand changes in urban carbon emissions and to assess the effectiveness of emission controls. Atmospheric CO2 observations, through data assimilation, can objectively invert changes in urban carbon emissions. In this study, inheriting the inversion method of a global carbon assimilation system of GCASv2, we constructed a regional carbon assimilation system based on a regional 3-D atmospheric chemical transport model WRF-CMAQ, named RegGCAS. We tested the performance of the established assimilation system for winter carbon emissions in the Yangtze River Delta (YRD) by performing a 9 km × 9 km resolution inversion in December 2020, using observations from 6 ground stations and two emission inventories as a priori, respectively. The results show that after assimilating the observations in the YRD region, especially in Suzhou and Hangzhou where there are more observing sites, the two significantly different a priori emissions are effectively converged, with the relative difference being reduced from −45 % and − 36 % of the a priori emissions to −10 % and − 15 % of the a posteriori emissions. Furthermore, the daily changes of the inverted emissions clearly show that weekend emissions are lower than weekday emissions, i.e. the inversion can reveal the weekend effect of emissions changes. This suggests that our system is well established and can be used for estimating fossil fuel carbon emissions during winter. Meanwhile, these results also imply that with our system, when a city has three or more stations, its total emissions can be constrained reliably.