Weather forecasts can help identify environmental conditions conducive to prescribed burning or to increased fire danger. These conditions are important components of fire management tools such as fire ignition potential maps, fire danger rating systems, fire behavior predictions, and smoke dispersion modeling. Fire managers use these tools to make decisions on when to conduct prescribed burns, how to manage wildfires, and how to pre-position fire suppression forces. Forecast weather conditions provide variables such as temperature, relative humidity, solar radiation, precipitation (or lack thereof), and wind—these are used in models to predict outbreaks of dry thunderstorms, the moisture content of fuels, and fire behavior. Forecast accuracy varies by model and by location; therefore, improved forecast accuracy, data accessibility, and tools to aid decision makers were identified as important gaps to be filled. In this project, we created a website and underlying system that combines meteorological observations and four weather forecasting systems with two fire danger rating systems to produce spatial forecasts of lightning probability, dry lightning probability, and fuel ignition potential over three- to seven-day forecast time periods. The system identifies potential forecast bias in the spatial domain of each of the meteorological forecast systems to aid in selecting the best fire weather forecast for the location of interest. Maps of fuel moisture, fire danger rating indices, and other parameters aid in assessing flammability status across the landscape. The results will aid decision makers in positioning resources, making “go/no go” decisions on prescribed burns, determining when fire might be used to meet resource management needs, and determining the local certainty of fire weather forecasts. Our system provides an easily accessible set of web-based products that include (1) color contour displays of sustained ignition probability at 2.5-km resolution for the western United States; (2) color contour displays of key meteorological parameter forecasts (at resolutions based on the model selected); and (3) meteorological model performance assessments of model bias, model reliability, and overall performance of predictions of fire weather variables used in predictions of ignition risk potential. By integrating fire weather forecasts, forecasted fire danger rating, lightning occurrence probabilities, rainfall predictions, and gridded fuels information. The system provides levels of confidence for fire weather forecasts, forecasts dry lightning probability, and forecasts fuel moisture conditions.