Abstract
<jats:p>Abstract. High-resolution three-dimensional (3D) ocean reanalysis data are essential for investigating multiscale ocean dynamics in the regional ocean and their impacts on energy transport, marine ecosystems, and climate change. The South China Sea (SCS), as one of the most eddy-active marginal seas in the global ocean, is characterized by complex 3D dynamical processes and frequent extreme ocean events, imposing urgent data demands on the scientific community. This paper presents a new South China Sea Ocean Reanalysis (SCSORA) dataset, with high resolution (1/30°) covering the period 2001–2024, which is generated by the second version of the South China Sea Operational Oceanography Forecast System (SCSOFSv2). SCSORA provides daily 3D fields of temperature, salinity, and current velocity, together with sea surface height (SSH).Three categories of observational data are assimilated into SCSOFSv2, including satellite-derived optimum interpolation sea surface temperature (OISST), along-track sea level anomaly (SLA) from AVISO, and in-situ temperature–salinity Argo profiles. Systematic validations against multisource satellite retrievals, in-situ observations, and existing reanalysis products demonstrates that SCSORA achieves satisfactory accuracy and reliability in reproducing sea surface temperature (RMSE: 0.34 °C), SLA (RMSE: 5.9 cm), and subsurface thermohaline structure. Kinetic energy spectral analysis reveals that SCSORA is capable of resolving ocean dynamical processes spanning from mesoscale to part of the submesoscale range. Representative applications demonstrate the potential of SCSORA in characterizing the spatiotemporal features of marine heatwaves (MHWs) in the SCS and in examining the 3D structural modulation of MHWs by mesoscale eddies, revealing distinct modulation mechanisms of different eddy polarities on the vertical structure of MHWs. SCSORA provides a critical 3D data foundation for the physical oceanography and extreme event research communities focusing on the SCS. It is publicly available at https://doi.org/10.12378/geodb.2026.2.005.V1 (Zhu et al., 2026).</jats:p>