Above-ground forest biomass at regional-level is typically estimated by adding model predictions of biomass from individual trees in a plot, and subsequently aggregating predictions from plots to large areas. There are multiple sources of uncertainties in model predictions during this aggregated process. These uncertainties always affect the precision of large area biomass estimates, and the effects are generally overlooked;however, failure to account for these uncertainties will cause erroneously optimistic precision estimates. Monte Carlo simulation is an effective method for estimating large-scale biomass and assessing the uncertainty associated with multiple sources of errors and complex models. In this paper, we applied the Monte Carlo approach to simulate regional-level above-ground biomass and to assess uncertainties related to the variability from model residuals and parameters separately. A nonlinear model form was used. Data were obtained from permanent sample plots and biomass observation of Cunninghamia lanceolata in JiangXi Province, China. Overall, 70 individual trees were destructively sampled for biomass estimation from June to September, 2009. Based on the commonly used allometric model, we conducted Monte Carlo simulations 1000 times for the biomass model fitting with the biomass data, from which we estimated the biomass of the plot data, and conducted an uncertainty assessment from the model residual variability and parameter variability. Estimates of above-ground biomass in JiangXi Province were obtained by aggregating model predictions of biomass for individual trees within plots, and then calculating the mean of the plots. Four modeling options with different sample sizes and R² were designed separately, from which Monte Carlo simulations were performed 1000 times and 2000 times, respectively, to study the effects of the model parameter and residual variability on the uncertainty in large-scale biomass estimates. The results revealed that the estimates of above-ground biomass and its uncertainty for C. lanceolata in JiangXi Province in 2009 achieved stability after 500 Monte Carlo simulations, and that the average biomass estimate was 19.84 t/hm², with additional uncertainty of 1.27 t/hm², representing 6.41% of the average biomass. With increasing modeling sample size from 30 to 60, the relative uncertainty of biomass estimates decreased from 14.86% to 9.85%, but the uncertainty variations for different levels of R² values minimally changed. We concluded that: 1) the Monte Carlo approach works well for regional-level estimations of biomass and its uncertainty based on forest inventory data;2) the uncertainty of biomass estimation in large areas should not be overlooked because of the large number of errors when extrapolating from the individual tree to the plot level in forest inventory data;3) with gradually larger modeling sample size, the average biomass increased while the uncertainty values decreased, and the operation times required for achieving the stability of average biomass and corresponding uncertainty in Monte Carlo simulations also were reduced, indicating that increasing modeling sample size is an effective way to reduce uncertainty in regional-level biomass estimations;and 4) model residual variability associated with R² was less important in model uncertainty of biomass estimates;however, higher R² does reduce the operation times for achieving stability of Monte Carlo simulations. This study is the first to apply the Monte Carlo simulation approach to estimating regional-level biomass and its uncertainty based on continuous observation data from permanent sample plots. This study is also the first to quantify the effects of uncertainty related to model parameters and residual variability in model predictions of extrapolating individual tree biomass to large area biomass estimates.