Public perceptions of the risk posed by natural hazards and climate change are typically associated with extreme events. While there are studies focused on the detailed synergy for processes that lead to the event occurrence. Past literature working on long-term variations of global weather and climate extremes often studies the derived indices and applies further statistical models to data sampled from a fixed grid point or area. Our study, using extreme heatwave events as an example, proposes an alternative framework to track and analyze extremes from an event perspective to facilitate better climate risk communication.

With a selected threshold of daily heatwave intensity and an objective spatial and temporal connectivity algorithm, we introduce an event-tracking method to automatically track all global heatwaves based on long-term reanalysis data. Two nearby but separate European heatwave events (Scandinavia and France) in 2003 are used to demonstrate the validity of our event-tracking method. We further highlight the genesis frequency, occurrence density, and tracks of all historical heatwave events over global land areas from 1979 to 2018. The contributions from heatwave intensity, the affected area, and temporal evolution to each extreme event are documented. With the retaining spatial and temporal evolutions of tracked heatwave events during their lifespans, the precursory and concurrent environmental conditions associated with the event can be further studied. The integrated size and scale measures of extreme heatwave events can be compared historically in a region and/or across different climate regimes. They also provide better information for the risk assessment on event-associated loss and damages, and can easily be extended to study compound hazards with intersections of different types of extreme events.

公众对自然灾害和气候变化带来的风险的看法通常与极端事件有关。虽然有研究侧重于导致事件发生的过程的详细协同作用。过去研究全球天气和气候极端事件长期变化的文献经常研究衍生指数，并将进一步的统计模型应用于从固定网格点或区域采样的数据。我们的研究以极端热浪事件为例，提出了一个替代框架，从事件的角度跟踪和分析极端事件，以促进更好的气候风险沟通。

通过选定的每日热浪强度阈值和客观的时空连通性算法，我们引入了一种事件跟踪方法，以根据长期再分析数据自动跟踪所有全球热浪。2003 年两个附近但独立的欧洲热浪事件（斯堪的纳维亚和法国）被用来证明我们的事件跟踪方法的有效性。我们进一步强调了 1979 年至 2018 年全球陆地区域所有历史热浪事件的发生频率、发生密度和轨迹。记录了热浪强度、受影响区域和时间演变对每个极端事件的贡献。随着所追踪的热浪事件在其生命周期中的空间和时间演变，可以进一步研究与该事件相关的先兆和同时发生的环境条件。极端热浪事件的综合规模和规模测量可以在一个地区和/或不同气候制度的历史上进行比较。它们还为事件相关损失和损害的风险评估提供了更好的信息，并且可以轻松扩展到研究具有不同类型极端事件交叉点的复合危害。