This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed NowcastingGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events. The code is available at \url{https://github.com/Cmeo97/NowcastingGPT}.

翻译：本文提出了一种基于Transformer生成模型的极端降水临近预报创新方法，即采用带有极值损失（EVL）正则化的NowcastingGPT模型。利用荷兰皇家气象研究所（KNMI）的综合数据集，本研究聚焦于高精度短期降水预测。我们提出了一种无需预设固定极值表示即可计算EVL的新方法，解决了当前模型在捕捉极端天气事件方面的局限性。通过定性与定量分析，证明了所提出的NowcastingGPT-EVL在生成精准降水预报方面的卓越性能，尤其在应对极端降水事件时表现突出。相关代码已开源至 \url{https://github.com/Cmeo97/NowcastingGPT}。