Direct adoption of nationwide generalized biomass models often causes systematic prediction errors when applied to forest biomass estimation in Hunan Province. This study developed localized multi–component compatible biomass model systems for Chinese fir, poplar and oak based on 461 felled–tree specimens sampled from 14 administrative cities of Hunan Province during 2022–2023. Individual allometric equations were first fitted via weighted least squares, and hierarchical compatible model structures were further established by algebraic component summation and proportional scaling under simultaneous equation constraints. The final total–biomass compatible equations were: Chinese fir: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.065662D^{1.750420}{H}^{0.780381}+0.024760{(D}^2{H)}^{0.774387}$; Poplar: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.046471D^{2.171796}{H}^{0.462424}+0.013116·{(D}^2{H)}^{0.822310}$; Oak: $B_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=0.0927573{\left(D^{2}H\right)}^{0.89013}+0.018969{\left(D^{2}H\right)}^{0.89162}$. R² values for total and aboveground biomass exceeded 0.92 and prediction accuracy exceeded 96%, with TRE within ±3% for all species. Root biomass models reached prediction accuracy higher than 78% with R² > 0.78. Stem, wood and bark sub–models achieved distinctly better fitting performance than branch and foliage sub–models, and strict hierarchical compatibility was guaranteed among total and component biomass predictions. Calibrated exclusively with local field data, these models effectively eliminate regional systematic estimation bias and only require readily measurable DBH and tree height as input variables, serving as reliable tools for regional forest biomass assessment and routine forest resource monitoring across Hunan Province.