Salinity, as one of the essential physical properties of seawater, is a common tracer differentiating water masses in the ocean, which often require relatively high-resolution datasets. Limited by the coverage of direct observations, however, high-resolution spatial and temporal salinity data are not always available, which hinders the fine application of salinity data in discerning ocean processes and improved modeling of ocean physics and biogeochemistry. To supplement the salinity database, we reconstructed sea surface salinity (SSS) with reasonably high spatial resolution (0.05° × 0.05°) over 2003–2020 in the South China Sea (SCS) with a machine learning algorithm based on a combination of MODIS-Aqua remote sensing data and a large cruise observation-based dataset. The reconstructed SSS has a mean absolute error (MAE) of 0.2 when compared with our underway observations with a corresponding root mean square error (RMSE) of 0.3. The MAE between station-based observations and our reconstruction was 0.5, and the RMSE was 0.7. These validations strongly suggest that our reconstruction is highly adequate, representing at most a quarter of the identified discrepancies compared to the remote sensing SSS or two other prevalent model-derived datasets. Based on our reconstruction, the SSS in the SCS is relatively low in coastal waters, but high in the ocean basin, with a seasonal pattern with a minimum in the summer and a maximum in the winter. This spatio-temporal distribution is well consistent with the observations and is affected by the Pearl River plume, sea surface circulation, and precipitation. Using our reconstructed SSS, we were able to successfully characterize the spreading of the Pearl River and Mekong River plumes and the intrusion of the Kuroshio Current from the Pacific Ocean into the SCS.

Against the background of climate warming, marine heatwaves (MHWs) and terrestrial drought events have become increasingly frequent in recent decades. However, the combined effects of MHWs and terrestrial drought on CO₂ uptake in marginal seas are still unclear. The East China Sea (ECS) experienced an intense and long-lasting MHW accompanied by an extreme terrestrial drought in the Changjiang basin in the summer of 2022. In this study, we employed multi-source satellite remote sensing products to reveal the patterns, magnitude, and potential drivers of CO₂ flux changes in the ECS resulting from the compounding MHW and terrestrial drought extremes. The CO₂ uptake of the ECS reduced by 17.0% (1.06 Tg C) in the latter half of 2022 and the Changjiang River plume region shifted from a CO₂ sink to a source (releasing 0.11 Tg C) in July-September. In the majority of the ECS, the positive sea surface temperature (SST) anomaly during the MHW diminished the solubility of CO₂ in seawater, thereby reducing CO₂ uptake. Moreover, the reduction in nutrient input associated with terrestrial drought, which is unfavorable to phytoplankton growth, further reduced the capacity of CO₂ uptake. Meanwhile, the CO₂ sink doubled for the offshore waters of the ECS continental shelf in July-September 2022, indicating the complexity and heterogeneity of the impacts of extreme climatic events in marginal seas. This study is of great significance in improving the estimation results of CO₂ fluxes in marginal seas and understanding sea–air CO₂ exchanges against the background of global climate change.

Against the backdrop of global warming, extreme precipitation events have become more frequent. In complex terrain regions, due to the vulnerability of their ecosystems, extreme precipitation events can lead to significant secondary disasters. Utilizing daily rainfall data from the National Meteorological Information Center of China and statistical analysis, this study explores the spatial and temporal distribution of extreme precipitation in the Central Southwest China (CSC) region. The temporal pattern of extreme precipitation in CSC shows a consistent trend, while the spatial distribution reveals an opposite phase between the northern and southern parts of CSC. Based on this, we propose a new method for constructing extreme precipitation prediction models for complex terrain regions based on physical mechanisms, and take CSC area as a study case. Instead of anonymous feature selection, this method improves the accuracy and stability of the model by studying the impact of sea–air interactions on extreme precipitation and then introducing it into deep learning. It was found that the sea surface temperature (SST) anomaly in the South Indian Ocean affects extreme precipitation in the CSC by influencing uplift, atmospheric instability, and moisture. The SST anomaly also affects the intensity of cross-equatorial airflow, which changes the trajectory of the Pacific–Japan teleconnection wave and impacts extreme precipitation. These findings provide a comprehensive and reliable approach for forecasting extreme precipitation in CSC and are further integrated into the extreme precipitation prediction models.