Prediction of marine conditions is notoriously challenging in the northern South China Sea (NSCS) due to inadequate observations in the region. The underwater gliders that were developed during the past decade may provide observing platforms that could produce required observations. During a field experiment, temperature/salinity (T/S) profiles from a set of underwater gliders were assimilated into a real-time marine forecasting system, along with the assimilation of climatological monthly mean Argo data to constrain the basin-wide model biases. The results show that, in addition to the reduction of the basin-wide model biases by the assimilation of the climatological monthly mean Argo data, the assimilation of glider-observed T/S profiles is efficient to reduce the local biases of the NSCS marine forecasting by as much as 28-31% (19-36%) in 24 h to 120 h forecasts for temperature (salinity) from sea surface to a depth of 1000 m. Our results imply that the real-time marine forecasting for the NSCS can largely benefit from a sustainable glider observing network of the NSCS in the future.