  In this paper, we divide the globe into a hexagonal grid and we extracted
time series of daily fire counts from each cell to estimate and analyze
worldwide fire season severity (FSS), here defined as the accumulated fire
detections in a season. The central question here is evaluating the accuracy of
time series forecasting methods to estimate short-term (months) and medium-term
(seasons) using only historical data of active fire detections. This approach
is simple, fast, and use globally available data, making it easier for large
scale prediction. Our results comprehend descriptive and predictive analyses of
the worldwide seasonal fire activity. We verified that in 99% of the cells, the
fire seasons have lengths shorter than seven months and that 57% have their
lengths decrease. We also observed a declining tendency in the number of active
fire counts during the seasons in 61% cells. However, some regions like the
Northeast Brazil and the West Coast of the USA present an increasing trend. We
verified that the forecasting error is lower than the mean FSS in 95% of the
cells, indicating clear predictability in the FSS.
