Prediction of fuel moisture across the landscape is a useful tool for planning prescribed burns and predicting the behaviour of wildfires. In the paper we describe a system for predicting the moisture content and flammability of dead fine fuels in forests. Outputs from the fuel moisture model can be used as inputs to fire behaviour models (e.g. Gould et al. 2008), which may be used to identify conditions suitable for conducting prescribed burns or to model the spread of wildfires (e.g. Finney 1998, Tolhurst et al. 2008).The system is based on an existing fuel moisture model (Matthews 2006) adapted to work with 6 km weather forecast grids (Engel and Ebert 2012). The moisture model represents fluxes of energy and water in a litter bed composed of three materials: litter, air, and free liquid water on the surfaces of the litter. The litter bed is bounded above by the atmosphere and below by the soil. The heat and water budget of each of the three materials is calculated at five equally spaced nodes within the litter layer using equations for six quantities: litter temperature, the temperature of free liquid water on the litter surfaces, air temperature, litter moisture content, amount of liquid water on litter surfaces, and specific humidity.Predictions of fuel moisture content and fuel availability are calculated hourly for forested areas for 4 to 7 days in advance. Forecasts are output as 3 dimensional grids (latitude, longitude, time), which may be visualised as maps or time series. A single set of predictions are made for each grid cell, based on the assumption of a forest on flat ground with the mean fuel load within a grid cell. Above canopy radiation is estimated from solar position (Meeus 1991) and forecast cloudiness (Iziomon and Mayer 2002). Weather forecasts are adjusted to account for modification of wind and radiation by the forest canopy (Silbertstein et al. 2001, Matthews et al. 2007). Fuel loads are derived from vegetation maps.Model output was tested against historical data sets (Matthews et al. 2007) and measurements made in the Blue Mountains region of NSW, Australia. The model was able to replicate these observations with some limitations and was able to represent differences in fuel wetting and drying as determined by fuel load and forest structure. Field observations have shown that sheltered, down slope locations may be much wetter than flat areas, which needs to be taken into account when interpreting predictions (Sullivan and Matthews 2012).Being able to make fuel moisture predictions that take into account small scale variations in weather and responds to changing vegetation will enable managers to improve the efficiency of planning of burning operations. Future work to improve the fuel moisture prediction system could include further testing against field measurements, refinement of forest structure parameters, inclusion of sub-grid scale variability, particularly slope and aspect effects, and inclusion of forecast uncertainty.