  Wildfire is an important system process of the earth that occurs across a
wide range of spatial and temporal scales. A variety of methods have been used
to predict wildfire phenomena during the past century to better our
understanding of fire processes and to inform fire and land management
decision-making. Statistical methods have an important role in wildfire
prediction due to the inherent stochastic nature of fire phenomena at all
scales. Predictive models have exploited several sources of data describing
fire phenomena. Experimental data are scarce; observational data are dominated
by statistics compiled by government fire management agencies, primarily for
administrative purposes and increasingly from remote sensing observations.
Fires are rare events at many scales. The data describing fire phenomena can be
zero-heavy and nonstationary over both space and time. Users of fire modeling
methodologies are mainly fire management agencies often working under great
time constraints, thus, complex models have to be efficiently estimated. We
focus on providing an understanding of some of the information needed for fire
management decision-making and of the challenges involved in predicting fire
occurrence, growth and frequency at regional, national and global scales.
