Forest dominant height is a fundamental structural attribute that reflects site conditions and forest growth potential. Here we present a nationwide 30 m resolution forest dominant height dataset for China (FDH-30C). The dataset is calibrated using 1,117 km² of high-density unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data distributed across all eight major forest ecozones across China as reference data, representing diverse stand ages, structures, and species compositions. To produce spatially continuous estimates, the model used in this dataset integrates 30 geospatial predictors derived from multi-source remote sensing products, including climatic, edaphic, topographic, vegetation, and Synthetic Aperture Radar (SAR)-based variables. A two-stage hybrid modeling framework combines the UAV LiDAR reference data with these predictors to generate spatially coherent estimates while preserving local accuracy and reducing ecozone boundary effects. The resulting map provides a consistent national baseline for applications such as site-index mapping, growth-and-yield parameterization, biomass and carbon estimation, vertical structure analysis, and the evaluation of spaceborne LiDAR missions.