Understanding and predicting increased fire risk is a complex task that hinges on various factors, including weather conditions, geographical location, and the presence of large-scale phenomena. In this exploration, we delve into the advancements in forecasting capabilities and the potential to extend predictions beyond the typical short and medium-range forecasts.

Most early warning systems currently in use rely on short and medium-range weather forecasts. These forecasts serve as the foundation for providing early warnings about the establishment of fire-prone conditions. However, their effectiveness is limited to relatively brief lead times.

How far in advance can we extend this prediction with sufficient skill? The answers to this question may be found in the recently released dataset resulting from a collaboration between the European Centre for Medium-Range Weather Forecast (ECMWF) and the Joint Reasearch Centre as the entrusted entity of the Copernicus Emergency Management Service (CEMS). We have broadened the range of fire danger data available in the Climate Data Store (CDS), introducing a set of fire danger forecasts with lead times of up to 7 months.

The dataset integrates fire danger indices from three different models developed in Canada, the United States, and Australia. These indices leverage the ECMWF Seasonal Forecasting System 5 (SEAS5). Preliminary analysis demonstrates that globally anomalous conditions for fire weather can be predicted with confidence up to 1 month ahead, and in some regions, this prediction can extend to 2 months.

Beyond the 2-month horizon, forecast accuracy tends to align closely with climatology. However, there are exceptions when anomalous fire weather is influenced by large-scale phenomena such as the El Niño Southern Oscillation and the Indian Ocean Dipole. These phenomena, known for their impact on weather patterns, can offer an extended predictability window of up to 6-7 months ahead.

While the science of predicting increased fire risk continues to evolve, the recent advancements in long-term forecasting, mark a promising step forward. As we navigate the complexities of fire prediction, understanding the influence of both short-term weather patterns and significant climatic phenomena becomes essential for developing more effective early warning systems and mitigating the risks associated with wildfires.

We welcome everyone to explore the dataset !