Abstract Accurate simulation of regional carbon dioxide (CO 2 ) concentrations is critical for urban carbon monitoring, inverse modeling and mitigation. However, large uncertainties persist due to differences in anthropogenic emission inventories in China. Focusing on Jiangsu Province in the Yangtze River Delta, where the mean inter‐inventory spread in annual city‐level total emissions exceeds 60% and spatial discrepancies are substantial, we assessed how six widely used inventories affect modeled CO 2 fields. Using a 3‐km WRF‐Chem‐VPRM framework, we performed simulations for July and December 2022. To isolate the impact of inventory‐related uncertainties, we designed sensitivity experiments that separately perturbed inventory selection, emission magnitude, spatial distribution, and temporal and vertical allocation used in the inventory‐to‐model matching. All experiments were driven by identical meteorological conditions to ensure comparability. Model outputs showed good consistency with meteorological observations, CarbonTracker near‐surface CO 2 , and OCO‐2 XCO 2 ( R ≈ 0.83), while also capturing physically reasonable near‐surface diurnal behavior. Inventory selection led to a nighttime urban domain‐averaged standard deviation of 8.2 ppm. Sensitivity results indicated that spatial allocation differences contributed more to modeled CO 2 variability than total emission magnitude. Under stable boundary‐layer conditions, vertical allocation emerged as a key uncertainty source, producing 25–50 ppm differences in surface CO 2 . These results demonstrate that inventory‐to‐model matching, especially vertical allocation, can exceed the impact of inventory selection for high‐resolution urban CO 2 simulations under stable nighttime conditions. This study provides a quantitative basis for diagnosing inventory‐induced variability and supports the development of fine‐resolution, vertically resolved inventories for robust urban CO 2 modeling and future inversion efforts.

Abstract Global warming has intensified heatwaves worldwide, yet their dependence on synoptic circulation remains poorly understood. Here, we identify land and coastal heatwaves during 1959–2023 using a Three‐Dimensional Connected Detection Algorithm, and classify their associated circulation patterns through an integrated EOF‐Kmeans method. We find that heatwave types are strongly associated with the position of anticyclones: coastal heatwaves occur when anticyclones are positioned across the boundary between land and ocean, while land heatwaves prevail when anticyclones are centered over continents. Under global warming, synoptic circulation patterns favorable for heatwave formation are amplified, while those suppressing heatwaves are weakened. Temperature decomposition further reveals that adiabatic heating acts positively (negatively) under anticyclonic (cyclonic) circulation, diabatic heating intensifies heatwaves under most patterns, and temperature advection generally suppresses heatwaves due to land‐sea thermal contrast over coastal regions. These findings highlight the essential role of synoptic circulation in shaping regional heatwave behavior and improving heatwave predictability in a warming climate.

Abstract Clouds cover approximately 60% of the globe and are therefore an obstacle to observing the atmosphere and surface of the Earth from space. To limit their impact on Infrared Atmospheric Sounding Interferometer (IASI)‐based atmospheric and surface property retrievals, it is important to obtain an IASI‐coherent cloud detection/classification. Many cloud retrievals, whether physical or statistical, are performed at the pixel‐level. However, since clouds are spatially structured, using the spatial coherency across the IASI footprints should improve cloud detection. Infrared Atmospheric Sounding Interferometer orbits (restructured as rectangular images) are collocated with the cloud classification (clear, water, ice, and two‐level ice) extracted from SEVIRI‐based Optimal Cloud Analysis to train a machine learning model. The training is performed over the SEVIRI disk, but the resulting model can be applied at the global scale (i.e., transfer learning). We use a partial‐Convolutional Neural Networks (p‐CNN), a new image‐scale model able to deal with a large amount of spatially missing pixels. This p‐CNN model correctly classifies the four cloud types with accuracy 77%; and this number increases to 88% when considering only spatially homogeneous IASI pixels, which shows the importance of subpixel heterogeneity. The other main source of differences is the IASI/SEVIRI resolution discrepancy. Thanks to our image‐processing approach and the cloud spatial coherency, two‐layer clouds are better retrieved than with pixel‐wise processing. Our new IASI cloud product not only classifies the cloud phase at a global scale, but also estimates the cloud‐type fractions in each IASI pixel. It therefore has a potential for subpixel downscaling.

Abstract Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants that pose significant risks to human health. Long‐term observations in PM 2.5 ‐bound PAHs are essential for understanding their source variations and assessing the impacts of emission control strategies. In this study, a total of 22 PAHs were measured at a rural site in the Pearl River Delta over a 12‐year period. Two distinct phases of variation were identified: During 2007–2013 (Phase I), PAH concentrations approximately doubled from 10.72 ± 4.77 ng m −3 to 23.56 ± 12.13 ng m −3 . In contrast, PAH concentrations decreased by 41% to 13.61 ± 7.17 ng m −3 during 2013–2018 (Phase II). Unexpectedly, an apparent increase in PAH mass fractions was identified (3% yr −1 ), which implied that PAH‐associated health risks did not decline despite improved air quality over the past decades. Correlation analysis and source apportionment revealed that PAHs originated primarily from coal combustion, accounting for 55% of the total contribution. In comparison, biomass burning and transportation accounted for 25% and 20%, respectively. Notably, the contributions of coal combustion significantly increased from 34% to 73%, while those of traffic emission and biomass burning decreased from 39% to 13% and from 27% to 14%, respectively. In addition, the persistently high PAH levels were primarily linked to coal combustion, resulting in PAH‐related health risks that remained approximately two orders of magnitude above the acceptable threshold. Our results highlight that future mitigation strategies should shift from a concentration‐based approach toward a health risk‐oriented framework that prioritizes the reduction of highly toxic components and their sources.

Abstract The subtropical high drives extreme weather and climate in mid‐low latitudes. While the summertime western Pacific subtropical high has been extensively studied, the hemisphere‐wide influence of the Tibetan Plateau (TP) on subtropical high remains less clear. Multi‐model simulations show that TP uplift intensifies and extends the Northern Hemisphere subtropical high northward in summer, while driving an overall intensification and expansion of the western Pacific subtropical high in winter. The mechanical effect of the TP dominates winter variability, whereas summer thermal forcing—peaking with diabatic heating—exerts a primary influence across the hemisphere. These impacts are mediated by atmospheric circulation. The reanalysis data further confirm the important role of TP thermal forcing in the observed variability of the subtropical high. Therefore, TP forcing is essential for understanding and predicting variability of the subtropical high throughout the Northern Hemisphere, beyond the western Pacific sector alone.

Abstract In recent years, compound hot‐drought events (CHDEs) have occurred with increasing frequency in the Yangtze River Valley in China. These extreme events have a severe effect on societal and ecological systems, as well as the economy. To identify the primary drivers of this decadal‐scale intensification of CHDEs, we present empirical and modeling evidence that demonstrates that the Atlantic Multidecadal Oscillation (AMO) exerts a dual effect on the decadal‐scale increase in CHDEs in the Yangtze River Valley. The AMO‐related high pressure over the northeastern flank of the South Asian High (SAH) directly contributes to its eastward extent. In addition, the AMO has facilitated the recent rapid warming of the Arabian Sea through the amplification of a positive wind‐evaporation‐sea surface temperature feedback. Warming in the northern Arabian Sea has further modified regional precipitation across the northern Arabian Sea and northwestern India. Subsequently, the pronounced interdecadal increase in summer precipitation has triggered anomalous diabatic heating, and thus strengthened the upper‐level West Asian High center on the circum‐global teleconnection system. It has also strengthened the downstream East Asian High center that extends the upper SAH. This study highlights the need to consider the dual effects of the AMO when investigating CHDEs.

No abstract is available for this article.

Abstract China has become a global hotspot of ozone (O 3 ) pollution, and understanding O 3 formation regime is crucial for air quality management. Satellite‐observed formaldehyde‐to‐nitrogen‐dioxide ratio (HCHO/NO 2 , FNR) has long been demonstrated as an efficient way to infer O 3 formation regime. However, reported FNR thresholds that divide O 3 formation regimes into VOC‐limited, transitional, and NO x ‐limited regimes uncover considerable differences. Using data from the world's first geostationary air quality monitoring instrument Geostationary Environment Monitoring Spectrometer, we report the spatiotemporal variations, particularly diurnal features, of O 3 ‐FNR relationships and O 3 formation regimes in China. We find that O 3 ‐FNR relationships fluctuate significantly over time and across locations, with coefficients of variation (CVs) of 0.22 and 0.27, respectively. We identified the amount of HCHO as a key factor. Given the strong correlation between NO 2 and LRO x /LNO x (chemical loss of HO 2 +RO 2 (LRO x ) to chemical loss of NO x (LNO x )), we demonstrate that O 3 formation regime thresholds alter minimally when column NO 2 is used as the indicator in China, with the CVs reduced to around 0.1. This finding highlights its potential to provide a more reliable diagnosis for large‐scale, space‐based O 3 formation sensitivity, offering promising insights for advancing O 3 pollution management in China.