The implications of using deep learning methods for extreme value analyses extend beyond environmental applications into various fields where quantifying rare events is critical. Deep learning algorithms offer advantages such as capturing complex non-linear relationships in data and scaling well to high-dimensional datasets. In extreme value analysis, these capabilities can lead to more accurate estimation of extreme quantiles and better understanding of risk factors associated with rare events. Beyond environmental settings like wildfires, deep learning methods can be applied in finance for modeling extreme market fluctuations, in healthcare for predicting rare diseases or adverse medical events, and in engineering for assessing structural risks during extreme conditions.