This study offers a valuable dataset of internal waves (IWs) in the northern South China Sea, employing advanced deep learning techniques to extract high-resolution data. The analysis of spatial and temporal IWs distributions, along with the identification of key patterns and regional characteristics, significantly enhances our understanding of IWs dynamics. However, some minor revisions are required before acceptance by ESSD.

(1) Confirm whether the abstract needs to mention the described data and include the DOI number.

(2) The introduction lacks sufficient detail about the South China Sea and does not adequately explain the reasons for studying this region. It would be beneficial to include information on the complex topography, circulation patterns, eddies, Kuroshio intrusion, and other dynamic processes. Additionally, consider discussing the current challenges in IWs research and the necessity of releasing this dataset to address these issues. Relevant references include Liu et al. (2016), and Xu et al. (2020).

Liu, T., Xu, J., He, Y., Lü, H., Yao, Y., & Cai, S. (2016). Numerical simulation of the Kuroshio intrusion into the South China Sea by a passive tracer. Acta Oceanologica Sinica, 35, 1-12.

Xu, J., He, Y., Chen, Z., Zhan, H., Wu, Y., Xie, J., ... & Cai, S. (2020). Observations of different effects of an anti-cyclonic eddy on internal solitary waves in the South China Sea. Progress in Oceanography, 188, 102422.

(3) For Figure 4, please clarify the criteria used for dividing the study area into Regions 1, 2, 3, and 4.

(4) The dataset primarily focuses on IWs in the South China Sea. It would be helpful if the authors could provide a rough estimate of the proportion of internal solitary waves versus internal tides in the dataset.

(5) Given the extensive accumulation of Argo profiles (or other observations), it would be interesting if the authors could provide a few examples matching identified IWs and show the vertical T/S profiles. This would demonstrate the actual presence of IWs and offer stronger validation of the dataset.

(6) Besides its use in validating numerical models, the dataset's potential applications could be further expanded. For example, it could be employed in studying IWs interactions with other processes, enhancing IWs monitoring and prediction systems, and supporting broader climate and oceanographic research. Expanding this discussion would underscore the dataset’s broader impact and versatility, encouraging its use across various fields.