Knowledge of vertical structural complexity (VSC) is important, because the resulting spatial partitioning is closely linked to resource utilization and environmental adaptation. How VSC responds to environmental changes on large scales and its mechanisms are poorly understood. We investigated 2,013 plant communities on the Tibetan Plateau (TP). VSC was quantified as the maximum height (Height-max), height variation (Height-var), and height evenness (Height-even). Precipitation dominated the VSC variation in forests and shrublands, supporting the classic physiological tolerance and hydraulic limitation hypotheses. In contrast, for alpine grasslands in extreme environments, non-resource limiting factors dominate VSC variation. Generally, with the shifting of climate from optimal to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors increases. Using machine learning models, maps of VSC at 1-km resolution were firstly produced for the TP. These findings provide new insights into macroecological studies, especially for adaptation mechanisms and model optimization.