Searching for the Most Extreme Temperature Events in Recent History

Because they are rare, extreme weather events have critical impacts on societies and ecosystems and attract public and scientific attention. The most unusual events are regularly documented as part of routine climate monitoring by meteorological services. A growing number of attribution studies also aim at quantifying how their probability has evolved under human-induced climate change. However, it is often recognized that (i) the selection of studied events is geographically uneven, and (ii) the definition of a given event, in particular, its spatiotemporal scale, is subjective, which may impact attribution statements. Here we present an original method that objectively selects, defines, and compares extreme events that have occurred worldwide in the recent years. Building on previous work, the event definition consists of automatically selecting the spatiotemporal scale that maximizes the event rarity, accounting for the nonstationary context of climate change. We then explore all years, seasons, and regions and search for the most extreme events. We demonstrate how our searching procedure can be both useful for climate monitoring over a given territory, and resolve the geographical selection bias of attribution studies. Ultimately, we provide a selection of the most exceptional hot and cold events in the recent past, among which are iconic heatwaves such as those seen in 2021 in Canada and in 2003 in Europe. SIGNIFICANCE STATEMENT: The purpose of the article is to objectively select and rank the most exceptional heatwaves and cold spells that have occurred worldwide in the recent years. As these events often have the greatest socioeconomic impacts, better knowledge and characterization of historical events in a changing climate can inform adaptation strategies. We exhaustively scan temperature data over all years and regions to identify extreme events using the event probability as a universal metric. Applied over a specific location, such as on a national level, our method provides useful information for the climate monitoring of weather events. Applied globally, it can help attribution studies to pick events without the selection bias related to authors origin or media coverage.