  Wildfire forecasting is notoriously hard due to the complex interplay of
different factors such as weather conditions, vegetation types and human
activities. Deep learning models show promise in dealing with this complexity
by learning directly from data. However, to inform critical decision making, we
argue that we need models that are right for the right reasons; that is, the
implicit rules learned should be grounded by the underlying processes driving
wildfires. In that direction, we propose integrating causality with Graph
Neural Networks (GNNs) that explicitly model the causal mechanism among complex
variables via graph learning. The causal adjacency matrix considers the
synergistic effect among variables and removes the spurious links from highly
correlated impacts. Our methodology's effectiveness is demonstrated through
superior performance forecasting wildfire patterns in the European boreal and
mediterranean biome. The gain is especially prominent in a highly imbalanced
dataset, showcasing an enhanced robustness of the model to adapt to regime
shifts in functional relationships. Furthermore, SHAP values from our trained
model further enhance our understanding of the model's inner workings.
