Burn severity is a key component of fire regimes and is critical for quantifying fires' impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China's forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., "Green firebreaks") is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.Read less <