Components of a fire regime have long been estimated using mean-value-based ordinary least-squares regression. But, forest and fire managers require predictions beyond the mean because impacts of small and large fires on forest ecosystems and wildland–urban interfaces are different. Therefore, different action plans are required to manage potential fires of varying sizes that demand size-based modelling tools. The objective of this study was to compare two model-fitting techniques, namely quantile mixed-effects (QME) model and ordinary linear mixed-effects (LME) model for constructing distributions of model-predicted small and large fires. I examined these techniques by modelling the fire size of individual escaped wildfires. Results showed that the LME-predicted fire size approximately coincided to the 0.75 quantile. The LME model produced more biased predictions at the two extremes, both of which manifest great importance in forest ecosystems and fire management. Modelling the distributions for small and large fires using quantile regression can reduce such biases along with giving unbiased mean estimates. This study concludes that quantile modelling is an effective approach to complement ordinary regression that helps predict the size-based risks of individual fires more precisely, and that could allow managers to better plan resources when managing fires.