Using remote sensing data to reconstruct three-dimensional (3D) temperature and salinity, a field called Deep Ocean Remote Sensing (DORS), is essential for the study of ocean dynamics and climate change. However, existing DORS studies predominantly focus on monthly scales and lack explainability, leaving the mechanisms governing reconstruction errors poorly understood and hindering daily-scale operational applications. Here we report a transformer-based framework (i.e., EarthFormer) to reconstruct daily 3D temperature and salinity fields from multi-source remote sensing inputs, across 19 standard depth levels in the Northwest Pacific (NWP) (105–160° E, 0–40° N) at 0.25° resolution using reanalysis model product as the labeled data. Validated against Argo observations, the reconstruction achieves an root-mean-squared error of 0.893 °C (R<sup>2</sup> = 0.989) for temperature and 0.141 PSU (R<sup>2</sup> = 0.827) for salinity, approaching reanalysis accuracy while offering near-real-time timeliness and lightweight computation. Notably, explainable AI analysis reveals that the counter-intuitively low contribution of satellite-derived sea surface salinity (SSS) stems not from weak physical relevance but from data-quality limitations; substituting high-accuracy SSS shifts the salinity error profile from a monotonic depth decrease to a V-shaped structure, with SSS contribution rising from ~10 % to ~50 %. Overall, this study demonstrates the feasibility of explainable-AI-enhanced daily 3D thermohaline reconstruction, providing a new technical pathway for real-time ocean monitoring and underscoring that improving satellite SSS retrieval and data quality is as critical as advancing model architectures for reliable DORS applications. The data are publicly available at <a href="https://doi.org/10.5281/zenodo.20602639" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.20602639</a> (Fang, 2026).