  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.
