Abstract: The water content of surface dead fuels is one of the most important indicators for forecasting fire danger and fire behaviors. We employed the timelag equilibrium water content methods (i.e. Nelson and Simard models) and the meteorological variable regression method to continuously measure the water content of surface dead fuels by one-hour time step from September to October in 2010 under Populus davidiana + Betula platyphylla, Picea koraiensis and the cutover lands (Pinus sylvestris var. mongolica + Betula platyphylla) with different canopy densities in Pangu Forestry Bureau, the Great Xing’an Mountains, Heilongjiang Province, China. We established prediction models and obtained prediction errors. The models were also used to extrapolate the water contents of surface dead fuels under other forest stands and the extrapolation accuracy was analyzed. The results showed that the mean absolute error, the mean relative error and the mean square error root of Nelson model (0.0154, 0.104 and 0.0226) were lower than those of Simard model (0.0185, 0.117 and 0.0256). In terms of extrapolation effects, the mean absolute error, the mean relative error and the mean square error root of meteorological variable regression method (0.0410, 0.0300 and 0.0740) were lower than those of Simard model (0.610, 0.492 and 0.846), but they were higher than those of Nelson model (0.034, 0.021 and 0.0660). Such results indicated that the timelag equilibrium moisture content method by one-hour time step, especially Nelson model, was sui-table for the forest stands in the Great Xing’an Mountains. Although extrapolation could not reduce the prediction errors, it could help improve the prediction accuracy and the efficiency of the present models applied to different forest stands or in a larger scale. The modeling and extrapolation errors were closely related to species identity and canopy densities, thus the appropriate timelag equilibrium moisture content methods should be selected according to different forest stands and locations.