The 2018 Camp fire destroyed the town of Paradise, California and resulted in 82 fatalities, the worst wildfire disaster in the US to date. Future disasters of similar or greater magnitude are inevitable given predicted climate change but remain highly uncertain in terms of location and timing. As with other natural disasters, simulation models are one of the primary tools to map risk and design prevention strategies. However, risk assessments have focused on estimation of mean values rather than predicting extreme events that are increasingly becoming a reality in many parts of the globe. Using the western US as a study area, we synthesized newer wildfire simulation and building location data (54 million fires, 25 million building locations) and compared the outputs to several sources of observed exposure and loss data. The simulations used synchronized weather among spatial simulation subunits, thereby providing estimates of extreme fire seasons, fire events within them, and exceedance probabilities at multiple scales. We found that annual area burned was accurately replicated by simulations but building exposure was substantially overestimated, although small observed sample size might have influenced the comparison. We identified extreme fire seasons in the simulation record (10,000 fire years) that exceeded historical fire seasons by 278% in terms of area burned, and 1,255% in terms of buildings exposed, under contemporary climate. Simulated building exposure peaked in specific regions along gradients of building density and burnable fuels. The study is the first to explore large scale extreme wildfire exposure in terms of both annual variability and magnitude, providing a broad foundation of methods to advance wildfire disaster prediction.