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Arctic sea ice plays an important role in regulating the global climate system. According to the IPCC-AR6 report, it is highly likely that the entire Arctic sea will be ice-free in summer before the middle of this century (Li et al., 2021). This has not only a significant impact on human production and life, but also brings opportunities for the development and utilization of Arctic resources (Mori et al., 2014;Stroeve et al., 2014). Accurate synoptic-scale Arctic sea ice prediction has become an urgent need for the planning and daily adjustment of vessel routes (Hebert et al., 2015). The sea ice concentration (SIC) is one of the most important parameters used to describe the surface thermal state of polar ocean. Data assimilation of SIC observations has been proven to be an effective method that can provide a more realistic initial model state for sea ice numerical models to improve short-term numerical forecasting (

Accurately inverting global and regional subsurface temperature (ST) by multisource satellite observations is a challenging but hot topic. This study proposes a new method to invert daily ST from the sea surface information in China's marginal seas based on generative adversarial network (GAN) model. The proposed GAN‐based model can project the STs from sea surface information (SLA, SSTA, SST) with a high resolution of 1/12°. A traditional regression‐based model, Modular Ocean Data Assimilation System (MODAS), is set up same experiments for comparison. The results show that the averaged root mean square error results are less than 1.45°C in the upper 200 m and the highest averaged R2 of 0.97 at the 70 m level, which is better than that of MODAS. Errors analysis and typical oceanographic phenomena analysis results show the superiority of the proposed GAN‐based model in this study. This study can provide high‐precision daily ST data from sea surface information, which can be expanded to further studies on the interior ocean variation characteristics.

Ingenious use of multisource satellite observations to accurately invert global and regional subsurface thermohaline structure is essential for understanding ocean interior processes, but extremely challenging. This study proposes a new method from the sea surface information inverting daily subsurface temperature (ST) based on generative adversarial network (GAN) model in China's marginal seas. The proposed GAN-based model can project the STs from sea surface information (SLA, SSTA, SST) with a high resolution of 1/12°. A traditional regression-based model, Modular Ocean Data Assimilation System (MODAS), is set up the same experiments for the sake of comparison. The results show that the averaged RMSE results are less than 1.45°C in upper 200m and the highest averaged R2 of 0.97 at the 70m level, which are better than that of MODAS. Errors analysis and typical oceanographic phenomena analysis results show the superiority of the proposed GAN-based model in this study. This study can provide high-precision daily ST data from sea surface information, which can be expanded to further studies on the ocean interior variation characteristics.

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