While much is known about heat extremes, drought extremes, and precipitation extremes individually, little is known about what controls their co-occurrence both regionally and globally. Flooding can result from both unusually intense precipitation events and unusually long-lived events however, when unusually long-lived events are also unusually intense in terms of their precipitation rate, flooding can be abrupt and extreme, leading to loss of life, property damage, and severely compromised infrastructure. Positive temperature anomalies coupled with high humidity can result in extreme heat index values, which can be detrimental to human health (Steadman 1979 Wehner et al. However, negative precipitation anomalies co-occurring with positive temperature anomalies can greatly exacerbate drought conditions due to the increased evapotranspirative demand placed on the system, i.e. For example, drought is commonly thought of as a result of only a lack of precipitation, i.e. However, the joint occurrence of two or more co-occurring extremes has the potential to negatively impact human and natural systems in ways far greater than any single event could (Leonard et al. Single meteorological or climatological extremes have a strong and disproportionate impact on societies, ecological systems, and natural environments. Of all the indicators of the large-scale climate conditions we studied, the dipole index explains the greatest fraction of multivariate variability in the co-occurrence of California wintertime extremes in temperature and precipitation. Further, we demonstrate that the multivariate statistics of temperature and precipitation are highly non-stationary and therefore require more robust and sophisticated statistical techniques for accurate characterization. We find that multivariate variability and statistics of temperature and precipitation exhibit strong spatial variation across scales that are often treated as being homogeneous. Using California wintertime (November–April) temperature and precipitation as a case study, we apply a novel, nonparametric conditional probability distribution method that allows for evaluation of complex, multivariate, and nonlinear relationships that exist among temperature, precipitation, and various indicators of large-scale climate variability and change. The large-scale drivers which modulate the occurrence of extremes in two or more variables remains largely unexplored. This was observed during the California drought of 2011–2015 where multiple years of negative precipitation anomalies occurred simultaneously with positive temperature anomalies resulting in California’s worst drought on observational record. Two or more spatio-temporally co-located meteorological/climatological extremes (co-occurring extremes) place far greater stress on human and ecological systems than any single extreme could.
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