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Abstract Improving the skill of medium-range (3–8 day) severe weather prediction is crucial for mitigating societal impacts. This study introduces a novel approach leveraging decoder-only transformer networks to post-process AI-based weather forecasts, specifically from the Pangu-Weather model, for improved severe weather guidance. Unlike traditional post-processing methods that use a dense neural network to predict the probability of severe weather using discrete forecast samples, our method treats forecast lead times as sequential “tokens”, enabling the transformer to learn complex temporal relationships within the evolving atmospheric state. We compare this approach against post-processing of the Global Forecast System (GFS) using both a traditional dense neural network and our transformer, as well as configurations that exclude convective parameters to fairly evaluate the impact of using the Pangu-Weather AI model. Results demonstrate that the transformer-based post-processing significantly enhances forecast skill compared to dense neural networks. Furthermore, AI-driven forecasts, particularly Pangu-Weather initialized from high resolution analysis, exhibit superior performance to GFS in the medium-range, even without explicit convective parameters. Our approach offers improved accuracy, and reliability, which also provides interpretability through feature attribution analysis, advancing medium-range severe weather prediction capabilities. 

Abstract This study explores how tornadic supercell soundings significantly differ from the same‐location and same‐hour baseline environment soundings, sampled from the days prior to or following the event. Permutation testing is used to identify whether sounding‐derived parameters mixed‐layer convective available potential energy and 0–1 km storm‐relative helicity are significantly different between the tornadic and baseline environment. Typically, in an environment with marginal values of certain key environmental parameters, anomalous values of those environmental parameters are more strongly associated with supercell tornadoes. Furthermore, many tornadic events already exhibit environmental conditions favorable for tornadic supercells a day prior to the event itself. Generally, supercell tornadoes that occur during typical peak tornadic activity time frames are easier to distinguish from baseline (non‐tornadic) environments compared to those occurring in other time frames. Spatiotemporal variations of distinguishability between tornadic and baseline environmental parameters add complexity to traditional parameter‐based fixed threshold forecasting.