We developed a statistical model to predict global wildfire patterns based on a parsimonious set of weather, vegetation, and anthropogenic variables. This model is designed within a DGVM (Dynamic Global Vegetation Model)-compatible framework and helps to forecast seasonal fire patterns across diverse regions. It's simplicity makes it valuable for climate  and fire management planning,  helping communities better prepare for and adapt to rising wildfire threats.