Data-driven weather forecast based on machine learning (ML) has experienced
rapid development and demonstrated superior performance in the global
medium-range forecast compared to traditional physics-based dynamical models.
However, most of these ML models struggle with accurately predicting extreme
weather, which is closely related to the extreme value prediction. Through
mathematical analysis, we prove that the use of symmetric losses, such as the
Mean Squared Error (MSE), leads to biased predictions and underestimation of
extreme values. To address this issue, we introduce Exloss, a novel loss
function that performs asymmetric optimization and highlights extreme values to
obtain accurate extreme weather forecast. Furthermore, we introduce a
training-free extreme value enhancement strategy named ExEnsemble, which
increases the variance of pixel values and improves the forecast robustness.
Combined with an advanced global weather forecast model, extensive experiments
show that our solution can achieve state-of-the-art performance in extreme
weather prediction, while maintaining the overall forecast accuracy comparable
to the top medium-range forecast models.

基于机器学习的数据驱动天气预报在全球中期预报方面表现出优于传统基于物理的动力模型的快速发展和卓越性能。通过数学分析，我们证明了使用对称损失如均方误差（MSE）会导致偏差预测和极值低估。为了解决这个问题，我们引入了 Exloss，一种执行非对称优化并突出显示极值以获得准确极端天气预报的新型损失函数。此外，我们引入了一种无需训练的极值增强策略 ExEnsemble，它增加了像素值的方差并提高了预报的鲁棒性。结合先进的全球天气预报模型，广泛的实验表明我们的解决方案在极端天气预测方面可以达到与顶级中期预报模型相当的准确性。