Wildfires are longstanding natural phenomena with significant impacts on ecosystems and communities. In recent years, Canada has experienced particularly severe wildfire effects, especially in British Columbia (BC), which has endured prolonged and impactful wildfire events. However, there is currently no specialized wildfire time-series dataset for BC that considers long-term temporal sequences and multiple driving factors, which are essential for data-driven approaches. To facilitate future research on data-driven wildfire risk and spread prediction, we have developed a dataset covering the entire BC province, encompassing 683 wildfire events from 2002–2023 at 500m resolution with daily observations. For each wildfire event, the dataset includes 20 driving factors, including vegetation status, meteorological factors, human activities, topographical features, and active fire detection. Based on this benchmark and similar datasets from other regions, we compared multiple deep learning models, including CNN-based, Transformer-based, and Mamba-based architectures, to explore the effectiveness of existing deep learning models in wildfire risk prediction. We found that model F1 scores were below 0.6, indicating that this new dataset presents a challenging non-linear modeling scenario that requires more advanced and tailor-designed deep learning models to improve wildfire risk prediction accuracy.