Atmospheric oscillations are known to drive the large‐scale variability of hydrometeorological extremes in Europe, which can trigger flood events and losses. However, to date there are no studies that have assessed the combined influence of different large‐scale atmospheric oscillations on the probabilities of flood losses occurring. Therefore, in this study we examine the relationship between five indices of atmospheric oscillation and four classes of flood losses probabilities at subregional European scales. In doing so, we examine different combinations of atmospheric oscillations, both synchronous and seasonally lagged. By applying logistic regressions, we aim to identify regions and seasons where probabilities of flood losses occurring can be estimated by indices of atmospheric oscillation with higher skill than historical probabilities. We show that classes of flood losses can be predicted by synchronous indices of atmospheric oscillation and that in some seasons and regions lagged relationships may exist between the indices of atmospheric oscillation and the probability of flood losses. Furthermore, we find that some models generate increased (or decreased) probability of flood losses occurring when the indices are at their extreme positive or negative phases. A better understanding of the effects of atmospheric oscillations on the likelihood of flood losses occurring represents a step forward in achieving flood resilience in Europe. For instance, improved early predictions of the indices that represent such atmospheric oscillations, or the evidence of a lagged relationship between their teleconnections and floods, can significantly contribute to mitigating the socioeconomic burden of floods.

中文翻译：

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天气会怎样？基于振荡指标的洪水损失预测

众所周知，大气振荡会导致欧洲极端的水文气象极端变化，从而引发洪水事件和损失。但是，迄今为止，尚无研究评估不同的大规模大气振荡对洪灾发生概率的综合影响。因此，在这项研究中，我们研究了欧洲次区域尺度上的五个大气振荡指标与四类洪水损失概率之间的关系。在此过程中，我们检查了同步和季节性滞后的大气振荡的不同组合。通过应用逻辑回归，我们旨在确定可以通过大气振荡指数以比历史概率更高的技能来估计发生洪灾损失的概率的区域和季节。我们表明，可以通过大气振荡的同步指标来预测洪水损失的类别，并且在某些季节和地区中，大气振荡的指数与洪水损失的概率之间可能存在滞后关系。此外，我们发现，当指标处于极端正值或负值时，某些模型会产生增加（或减少）的洪灾损失概率。更好地了解大气振荡对洪水泛滥的可能性的影响，代表了在欧洲实现洪水抗灾能力的进步。例如，改进的代表此类大气波动的指数的早期预测，或者其遥相关与洪水之间关系滞后的证据，