Large-scale fire danger assessment has become increasingly relevant in the past few years, and is usually based on weather information. Still, fuel characteristics also play an important role in fire behavior. This study presents a fire behavior simulation based on a global fuelbed dataset and climatic and topographic information. The simulation was executed using the Fuel Characteristic Classification System (FCCS). The climatic information covered the period 1980-2010, and daily weather parameters were used to calculate the mean monthly fuel moisture content (FMC) and wind speed for the early afternoon period. Also, as the most severe fires occur with extreme environmental conditions, a worst-case scenario was created from the 30 days of each month with the lowest FMC values for the 1980-2010 period. The FMC and wind speed information was grouped into classes, and FCCS was used to simulate the reaction intensity, rate of spread and flame length of the fuelbeds for the average and worst-case monthly conditions. Outputs of the simulations were mapped at global scale, showing the variations in surface fire behavior throughout the year, both due to climatic conditions and fuel characteristics. The surface fire behavior parameters identified the fuels and environmental conditions that produced more severe fire events, as well as those regions where high fire danger only occurs in extreme climatic conditions. The most severe fire events were found in grasslands and shrublands in tropical dry biomes, and corresponding with the worst-case scenario environmental conditions. Also, the results showed the importance of including detailed fuel information into fire danger assessment systems, as the same weather and topographic conditions may have different danger rates, depending on fuel characteristics.