<p>The advent of industrialization has led to a significant increase in the frequency of extreme weather events, profoundly impacting human productivity and well-being. Computers excel at processing large-scale data, particularly in analyzing complex natural disasters such as wildfires, which are influenced by multiple interrelated factors. The emerging field of deep learning in computer science is particularly well-suited for this task. To date, deep learning has achieved notable success in wildfire forecasting through the analysis of remote sensing images. However, existing models often exhibit limited accuracy in wildfire prediction due to their inability to account for the varying importance of different input variables in influencing prediction outcomes. To address these limitations, we propose the Deformable Average Channel and Spatial Attention (DACSA) model for analyzing wildfire drivers, along with the integration of Location-aware Adaptive Normalization (LOAN) for wildfire prediction. This study represents the first application of an attention mechanism to this task, particularly in quantifying the driving factors of wildfires. By leveraging DACSA and LOAN, we can better identify and prioritize the factors influencing wildfire occurrence, thereby improving both the study and management of wildfires. We conducted extensive experiments on the FireCube dataset, comparing our LOAN with DACSA model against state-of-the-art models such as TimeSformer and SwinTransformer. Additionally, we benchmarked the DACSA model against mainstream attention mechanisms, including SimAM, SE, CA, CBAM, and ScConv, in terms of accuracy and computational efficiency. The experimental results demonstrate the superior performance of our proposed method, highlighting its potential for advancing wildfire prediction and management.</p>