Global risk of forest fires is amplified by the climate change driven heat waves, leading to more intensive biomass burning, which create a vicious cycle by accelerating the climate change. Despite of the growing risk of forest fires, a response system in South Korea, where more than 60% of its land is forest, is still focusing on posterior measures. To improve preventive measures, forest fire model needs to be developed for assessment of future risks of forest fires and burned areas. In this context, this study aims at optimization of the IIASA’s FLAM – a processed based model integrating both biophysical and human impacts – to the environment of South Korea for projecting the pattern and scale of future forest fires. The following model developments were performed in the study: 1) optimization of probability algorithms in FLAM, including ignition probabilities conditional on population density, lightning frequency, and fuel taking into account distance to cropland, based on the national GIS data downscaled to 1 km2, and 2) improvement of fuel moisture computation by adjusting Fine Fuel Moisture Code (FFMC) used by FLAM to represent feedbacks with vegetation; this was done by fitting soil moisture to the daily remote sensing data, 3) deeper look at the fire frequency in addition to areas burned simulated by FLAM. Our results show that the optimization has considerably improved the modelling of seasonal pattern of forest fire frequency. After optimization Pearson’s correlation coefficient between monthly predictions and observations from national statistics was improved from 0.171 in non-optimized version to 0.893 in the optimized version of FLAM. These findings imply that even though FLAM already contained main algorithms for interpreting biophysical and human impact on forest fire at a global scale, they were applicable to South Korea only after optimization of all its modules. In addition, as the optimization succeed to reproduce the national specific pattern of forest fire, it should be followed by the research for developing adaptation strategies corresponding to the projected risks of future forest fires.