FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction

TL;DR

FireCastNet is a novel deep learning model that uses Earth-as-a-graph and GNNs to improve global wildfire prediction up to six months ahead, outperforming existing models by capturing complex spatio-temporal dependencies.

Contribution

The paper introduces FireCastNet, combining 3D convolutional encoding with Graph Neural Networks to model Earth as an interconnected graph for enhanced wildfire forecasting.

Findings

FireCastNet outperforms state-of-the-art models in burned area prediction.

Longer input time-series improve forecast robustness.

Spatial context integration enhances extended horizon predictions.

Abstract

With climate change intensifying fire weather conditions globally, accurate seasonal wildfire forecasting has become critical for disaster preparedness and ecosystem management. We introduce FireCastNet, a novel deep learning architecture that combines 3D convolutional encoding with GraphCast-based Graph Neural Networks (GNNs) to model complex spatio-temporal dependencies for global wildfire prediction. Our approach leverages the SeasFire dataset, a comprehensive multivariate Earth system datacube containing climate, vegetation, and human-related variables, to forecast burned area patterns up to six months in advance. FireCastNet treats the Earth as an interconnected graph, enabling it to capture both local fire dynamics and long-range teleconnections that influence wildfire behavior across different spatial and temporal scales. Through comprehensive benchmarking against state-of-the-art models including GRU, Conv-GRU, Conv-LSTM, U-TAE, and TeleViT, we demonstrate that FireCastNet achieves superior performance in global burned area forecasting, with particularly strong results in fire-prone regions such as Africa, South America, and Southeast Asia. Our analysis reveals that longer input time-series significantly improve prediction robustness, while spatial context integration enhances model performance across extended forecasting horizons. Additionally, we implement local area modeling techniques that provide enhanced spatial resolution and accuracy for region-specific predictions. These findings highlight the importance of modeling Earth system interactions for long-term wildfire prediction.