Background Weather conditions play a crucial role in driving fire activity in Mediterranean France. Previous research has demonstrated the influence of these conditions on the likelihood of large fire events over the world. However, certain limitations persist regarding the representation of fire weather in probabilistic models. Aim The objective of this paper is to develop an efficient method to rate fire danger by identifying the best representation of weather data for fire activity prediction in Mediterranean France. Methods We evaluated the performance of meteorological variables and the most common fire-weather indices (FWIs) worldwide as predictors of fire occurrence and size using the Firelihood framework, a probabilistic Bayesian model of fire activity. These models were compared to a fire activity baseline model incorporating only spatial and temporal effects but no explicit fire-weather information to allow for an in-depth study of information not captured by fire-weather indices. Key results The results indicate that relying solely on fire-weather indices is insufficient for efficient rating of fire activities. The inclusion of spatial and seasonal effects in the models is crucial for improving the indices' performance. While the Canadian FWI remains the most skillful indicator among the tested indices, using new combinations of several of its subcomponents further increases accuracy. Various performance analyses, including threshold selections, were carried out to comparatively assess those improvements. Conclusions The approach shows that probabilistic models informed with appropriately constructed fire-weather indices substantially improve various aspects of fire activity predictions in the Mediterranean area.