Abstract: This paper aims at accurately predict the smoke temperature in a single-room fire. Since both high-fidelity simulations and single-fidelity surrogate models cost much computational time, it is hard to meet the emergency needs of fire safety management. Therefore, a multi-fidelity model named CoKriging was introduced , which made use of the simulation data from Consolidate Fire and Smoke Transport (CFAST) and Fire Dynamic Simulator (FDS) for training. The leave-one-out cross-validation suggests that this model has been effectively trained when the data ratio of CFAST to FDS is 10∶1. Further comparisons among different methods show that the prediction accuracy of CoKriging is comparable to that of artificial neural network (ANN) and Kriging, while the modeling time is only 1/10 of the latter. Additionally, the predicted temperatures of CoKriging are very close to the simulated results of FDS, and once the CoKriging model is successfully constructed, much less time will be taken to make a new prediction than that of FDS. The exploratory research on the proportion of high-and low-fidelity data to the prediction results of CoKriging shows that there is no obvious correlation between them, and the prediction accuracy can still be ensured even if only a small amount of FDS data participates in model testing. In conclusion, the CoKriging model could be used as a fast and effective regression analysis method for the temperature prediction in a single-room fire.