In the last decade, forest fires have become a natural disaster in Portugal, causing great forest devastation, leading to both economic and environmental losses and putting at risk populations and the livelihoods of the forest itself. In this work, we present Bayesian hierarchical models to analyse spatio-temporal forest fire data on the proportion of burned area and the number of fires jointly modeled. We look for space and time effects on that proportion and counts among Portuguese municipalities or districts over last years. Conditionally on the existence of forest fires for specific time and region, we assume different probabilistic model to the outcomes of interest, especially a mixture of distributions to model jointly the proportion of area burned and the excess of no burned area for early years. For getting estimates of the model parameters, we have used Monte Carlo Markov chain methods, as well as for some short term prediction. This is joint work with Paulo Soares. This work was supported by FCT Project UIDB/00006/2020.