【Objective】 Linear quantile regression and quantile groups were used in this study to model and predict crown width, which provides a valuable method for accurately simulating and predicting crown growth. 【Method】 Data in this study were collected from the natural forests of Larix gmelinii of Greater Khingan Mountains. Linear regression and quantile regression were used to build the basic and multivariate models of crown width. Prediction effects of seven quantiles groups were compared, namely three quantiles groups (τ=0.1, 0.5, 0.9 and τ=0.3, 0.5, 0.7), five quantiles groups (τ=0.1, 0.3, 0.5, 0.7, 0.9 and τ=0.3, 0.4, 0.5, 0.6, 0.7), seven quantiles groups (τ=0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.9 and τ=0.1, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9), and nine quantiles groups (τ=0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9). Effects of four sampling methods (random sampling, largest DBH tree selection, mean DBH tree selection and smallest DBH tree selection) and nine sampling sizes (1-9 trees per plot) on prediction accuracy were analyzed. K-fold cross validation was used to compare the prediction effects of linear regression, optimal quantile regression and optimal quantile groups. 【Result】 Linear regression and quantile regression fit the crown width models. The fitting results of the median regressions were similar to those of the linear regressions and were the best of all quantiles. The fitting and validation results of the multivariate model and quantile regressions were better than those of the basic model. Crown width was positively correlated with DBH and average tree height (site quality) and negatively correlated with height under banch (tree size) and basal area of larch (competition). Quantile groups could improve the predictive ability of the model. The seven quantile groups showed little difference. The three quantile groups (τ=0.3, 0.5, 0.7) had the best prediction ability. For the practical application of the basic and multivariate quantile groups, the optimal sampling design was to select the largest trees. A recommendation was to select six sample trees for each plot.【Conclusion】 Crown width models based on linear quantile groups can improve the prediction accuracy. A recommendation is to use three quartile groups and a sampling design of the six largest trees to predict crown width.