Background: The prediction accuracy for the rate of surface fire spread varies in different regions; thus, increasing the prediction accuracy for local fuel types to reduce the destructive consequences of fire is critically needed.

Aims: The objective of this study is to improve the Rothermel model’s accuracy in predicting the ROS for surface fuel burning in planted forests of Pinus koraiensis in the eastern mountains of north-east China.

Methods: Fuel beds with various fuel loads and moisture content was constructed on a laboratory burning bed, 276 combustion experiments were performed under multiple slope conditions, and the ROS data from the combustion experiments were used to modify the related parameters in the Rothermel model.

Results: The surface fire spread rate in Pinus koraiensis plantations was directly predicted using the Rothermel model but had significant errors. The Rothermel model after modification predicted the following: MRE = 25.09%, MAE = 0.46 m min−1, and R2 = 0.80.

Conclusion: The prediction accuracy of the Rothermel model was greatly enhanced through parameter tuning based on in-lab combustion experiments

Implications: This study provides a method for the local application of the Rothermel model in China and helps with forest fire fighting and management in China.