近日，中国科学院大气物理研究所刘屹岷团队报道了次季节提前期北半球中高纬度地区的热浪预报技能：区域和持续时间差异。该研究于2025年12月11日发表在《中国科学：地球科学》杂志上。

准确预测热浪至关重要，并且仍然是分季节到季节（S2S）预测项目的关键目标。然而，关注极端情况而忽视假警报可能会高估区域预报技能。因此，评估当前S2S模型在预测热浪方面的整体性能是必要的。

研究组利用当前多个分季节预报系统的长期数据集，评估了北半球中高纬度地区热浪的预报能力。分析表明，在半球尺度上，热浪的频率、强度和持续时间可提前2周预测。S2S模式一般会提前1-2周预测热浪的发生，提前3-4周预测暖异常的发生。该研究发现了两种显著的模式：区域依赖和持续时间效应。前者的例子是欧洲的热浪比东亚和北美的热浪更容易预测，这归因于更高的固有可预测性和更强的模式性能。后者反映了一个事实，即在前两周内，长时间热浪比短时间热浪更容易预测，这主要是由于大气可预测性的限制。这项研究提供了对当前S2S热浪预报能力的更细致的理解。

附：英文原文

Title: Heatwave forecast skill in the mid-high latitudes of the Northern Hemisphere at subseasonal lead times: Regional and duration disparity

Author: Haozhou LIU, Yimin LIU, Qing BAO, Bian HE, Wansuo DUAN

Issue&Volume: 2025/12/11

Abstract: Accurate prediction of heatwaves is critical and remains a key objective of the Sub-seasonal to Seasonal (S2S) Prediction project. However, focusing on extreme cases and neglecting false alarms might overestimate regional forecast skill. Therefore, it is essential to evaluate the overall performance of current S2S models in predicting heatwaves. Using long-term datasets from current multiple subseasonal forecast systems, this study assessed the forecast skill for heatwaves in the mid-high latitudes of the Northern Hemisphere. Analysis revealed that heatwave frequency, intensity, and duration can be predicted up to 2 weeks in advance at the hemispheric scale. The S2S models generally predict the occurrence of heatwave 1–2 weeks in advance and warm anomalies 3–4 weeks ahead. Two notable patterns were identified: regional dependence and a duration effect. The former is exemplified by heatwaves in Europe being more predictable than those in East Asia and North America, which is attributed to higher inherent predictability and stronger model performance. The latter reflects the fact that long-duration heatwaves are more predictable than short-duration ones within the first lead 2 weeks, primarily owing to atmospheric predictability constraints. This study provides a more nuanced understanding of the current capabilities of S2S heatwave forecasts.

DOI: 10.1007/s11430-025-1739-8

Source: https://www.sciengine.com/SCES/doi/10.1007/s11430-025-1739-8