  In this paper we discuss and address the challenges of predicting extreme
atmospheric events like intense rainfall, hail, and strong winds. These events
can cause significant damage and have become more frequent due to climate
change. Integrating climate projections with machine learning techniques helps
improve forecasting accuracy and identify regions where these events become
most frequent and dangerous. To achieve reliable and accurate prediction, we
propose a robust neural network architecture that outperforms multiple
baselines in accuracy and reliability. Our physics-informed algorithm heavily
utilizes the whole range of problem-specific physics, including a specific set
of features and climate projections data. The analysis also emphasizes the
landscape impact on the frequency distribution of these events, providing
valuable insights for effective adaptation strategies in response to climate
change.
