For investigation of internal solitary waves (ISWs) in the South China Sea (SCS), most cruise observations are concentrated from Luzon Strait to Dongsha Atoll in the northeastern SCS but few on the continental slope far away from the west of Dongsha Atoll. In this study, we use 1‐year long mooring data to determine dynamic and statistical features of the ISWs on the shelf slope of the northwestern SCS. The analysis results of the mooring data reveal that the ocean internal waves on the shelf slope of the northwestern SCS have physical properties of highly nonlinear waves, which are well described by the solutions of the Korteweg‐de Vries equation. The mean nonlinear phase speeds of mode‐1 and mode‐2 ISWs are 1.38 ± 0.14 and 0.66 ± 0.12 m/s, respectively. The major direction of mode‐1 ISWs is northwestward 305° ± 21°. Strong ISW currents force the major direction of total current velocities to turn 67.5° in the upper layer and 135° in the lower layer. The monthly occurrence frequency distribution of ISWs shows a peak in July with a maximum frequency of 16.2% and a trough in March with a minimum frequency of 3.3%. Mode‐2 ISWs appear most in December, accounting for 50% of total mode‐2 ISWs. The largest mode‐2 ISW on record up today with the depressed amplitude as large as 91 m, and the elevated amplitude of 73 m was observed at mooring station. These new findings and new data are of significance to local internal wave prediction model development.

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High-resolution salinity information is of great significance for understanding the marine environment. We here propose a deep learning model denoted the “Attention U-net network” to reconstruct the daily salinity fields on a 1/4° grid in the interior of the South China Sea (SCS) from satellite observations of surface variables including sea surface salinity, sea surface temperature, sea level anomaly, and sea surface wind field. The vertical salinity profiles from the GLORYS2V4 reanalysis product provided by Copernicus Marine Environment Monitoring Service were used for training and evaluating the network. Results suggest that the Attention U-net model performs quite well in reconstructing the three-dimensional (3D) salinity field in the upper 1000 m of the SCS, with an average root mean square error (RMSE) of 0.051 psu and an overall correlation coefficient of 0.998. The topography mask of the SCS in the loss function can significantly improve the performance of the model. Compared with the results derived from the model using Huber loss function, there is a significant reduction of RMSE in all vertical layers. Using sea surface salinity as model inputs also helps to yield more accurate subsurface salinity, with an average RMSE near the sea surface being reduced by 16.4%. The good performance of the Attention U-net model is also validated by in situ mooring measurements, and case studies show that the reconstructed high-resolution 3D salinity field can effectively capture the evolution of underwater signals of mesoscale eddies in the SCS. The resolution and accuracy of sea surface variables observed by satellites will continue to improve in the future, and with these improvements, more precise 3D salinity field reconstructions will be possible, which will bring new insights about the multi-scale dynamics research in the SCS.