Biomass burning has critical ecological and social impacts. Recent changes in climate patterns and land use have involved alterations of traditional fire regimes, which have increased the negative impacts of fire. Live Fuel Moisture Content (LFMC) has proven to be one of the main factors related to fire risk, as it affects fire ignition and fire behavior, and therefore it is an essential indicator for fire risk assessment. The aim of our research was to explore several methods to convert LFMC into Ignition Probability (IP) at a national scale, considering climate and vegetation functional types. The project covers the Iberian Peninsula territory of Spain (492 175 km2), for a ten year period. The LFMC data was estimated from NOAA-AVHRR imagery, whereas fire occurrence was based on the standard MODIS Thermal Anomalies product (MOD14). Non-parametric significance tests, histograms and percentiles, classification trees, and logistic regression models were used for estimating the IP from five variables based on LFMC. These modelling approaches were compared and Logistic Regression (LR) analysis was found to be most advantageous, since it uses several predictor variables to compute a continuous probability of IP. The area under the ROC curve of the LR models for the Iberian Peninsula was 0.65 for the Mediterranean region and >0.8 for the Eurosiberian region. The LFMC from one week before the fire detection was the most influential variable in the statistical analysis and it was the main variable in the Mediterranean models. In the Eurosiberian models, the LFMC decrement since spring was also important. The LFMC one week before the fire detection and the difference between the LFMC one week and two weeks before the fire detection were included in the grassland model. Shrubland is less susceptible to rapid moisture changes than grassland, so the LFMC from two weeks before the fire and the LFMC decrement since spring were more influential.