The assessment of fuel moisture content on a large spatial scale requires several observations and estimates and is often time consuming and costly due to labour and transportation expenses. Therefore, various models based on empirical functions of weather variables have been developed and applied to determine the amount of moisture in fuel. In this paper, a fuel dryness index (Fd) based on biophysical principles associated with energy exchange is presented and applied to monitor fuel moisture content for annual grasslands. Daily values of Fd were determined as the ratio of sensible heat flux density to the available energy using high-frequency temperature data and the surface renewal (SR) method in combination with net radiation and soil heat flux values. The SR method was evaluated by comparing with sensible and latent heat flux densities from eddy covariance data measured in a fire-vulnerable annual grassland. The Fd values and trends were compared with three well-known slow response fire-danger indices including the Keetch-Byram drought index, two modified versions of the drought factor in the McArthur forest fire-danger meter, and the fast response fine fuel moisture code of the Canadian fire weather index. Moreover, Fd index was compared with the McArthur grassland fire-danger meter. The Fd index was more responsive to daily changes than most of the other indices, providing accurate information on fuel dryness condition of a live vegetation grassland. In addition, it can potentially eliminate the need for calibrated empirical weather models and fuel stick measurements.