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Based on field inventory data collected from 117 temporary plots in Sichuan Province, 19 bamboo culm height–diameter at breast height (DBH) base models for Cizhu (Bambusa emeiensis) were constructed and assessed to evaluate the effectiveness and applicability of multiple model structures and re-parameterization strategy for model performance improvement. Under a unified evaluation framework (taking the R², RMSE, and AIC as criteria), the impact of model structure on fitting and predictive performance was analyzed. Based on partial correlation analysis and field operability, the branch-free culm node number was selected as an explanatory variable and used to re-parameterize each base model at each parameter position. The performance improvement achieved through re-parameterization for different model structures was systematically assessed. The results showed that at the base model level, the overall performances of most models were roughly similar, with the growth model performing relatively better according to comprehensive evaluation indicators (R²: 0.5764, RMSE: 2.376 m, AIC: 1109.19). As for re-parameterized models, they generally exhibited varying degrees of performance improvement compared to their corresponding base models, among which the growth model re-parameterized at the position of parameter b showed the best performance according to comprehensive evaluation indicators (R²: 0.6445, RMSE: 2.195 m, AIC: 1071.87). Re-parameterization based on growth structure variables can substantially enhance the fitting and prediction performance of bamboo height–DBH models for B. emeiensis. It is concise and easy to implement, which may provide reference for bamboo height–DBH modeling and other related research on B. emeiensis. Full article