Global controls on month-by-month fractional burnt area (2000-2005) were investigated by fitting a generalised linear model (GLM) to Global Fire Emissions Database (GFED) data, with 11 predictor variables representing vegetation, climate, land use and potential ignition sources. Burnt area is shown to increase with annual net primary production (NPP), number of dry days, maximum temperature, grazing-land area, grass/shrub cover and diurnal temperature range, and to decrease with soil moisture, cropland area and population density. Lightning showed an apparent (weak) negative influence, but this disappeared when pure seasonal-cycle effects were taken into account. The model predicts observed geographic and seasonal patterns, as well as the emergent relationships seen when burnt area is plotted against each variable separately. Unimodal relationships with mean annual temperature and precipitation, population density and gross domestic product (GDP) are reproduced too, and are thus shown to be secondary consequences of correlations between different controls (e.g. high NPP with high precipitation; low NPP with low population density and GDP). These findings have major implications for the design of global fire models, as several assumptions in current models - most notably, the widely assumed dependence of fire frequency on ignition rates - are evidently incorrect.