The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d²) and local statistics model matrix (LSM).

Numerous studies have demonstrated that climate exerts a substantial impact on forest growth. Traditional stand growth yield tables fail to take climatic factors into account, resulting in low accuracy and limited applicability. The study aimed to develop stand growth tables under climate change scenarios by incorporating the effects of climatic factors on stand growth, based on five successive permanent plot monitoring of natural Quercus mongolica broad-leaved mixed forests in Jilin Province from 1994 to 2014. First, the optimal growth models were selected from common theoretical growth equations to establish the site class curve. Then, growth equations for stand quadratic mean diameter at breast height (Dg), number of trees per hectare (N), and stand volume per hectare (V) were fitted separately. Climate-sensitive stand growth tables were compiled by site class using the simultaneous equations method, and stand growth dynamics were predicted under four climate scenarios (SSP126, SSP245, SSP370, SSP585) from 2011 to 2100. The results showed that model fitting identified the Logistic function as optimal for stand mean height and Dg, an exponential function for N, and the Richards function for V. Stand mean height was positively correlated with mean annual precipitation. Dg was positively correlated with mean temperature of the warmest month and mean annual precipitation. N was negatively correlated with mean temperature of the warmest month and showed no significant correlation with mean annual precipitation. V was negatively correlated with mean temperature of the warmest month and positively correlated with mean annual precipitation. The climate-sensitive stand growth tables improved long-term prediction accuracy compared with conventional static yield tables. This study provides dynamic and reliable technical support for the scientific management and ecological regulation of Quercus mongolica forests under climate change, and it also offers methodological references for compiling growth tables of other broad-leaved mixed forests.