It is a well-established fact that the present climate is changing as a result of human activities, the implications of which we are just now beginning realize. From record breaking heatwaves, wildfires, and extreme droughts, to sea level rise, ocean acidification, and severe flooding, there is little doubt that the consequences of anthropogenically driven climate change are wide and far reaching. One of the most confounding problems to date in understanding the effects of a changing climate is the absence of a world with a climate free from human interference to which to compare. The chaotic nature of atmospheric motions, naturally occurring low-frequency oscillations, and extreme events are all an inherent part of the climate system, and within that naturally occurring variability exists the signature of human interference with the global climate system. Thus the overarching aim of this dissertation is to describe, understand, and quantify the nature of extreme meteorological events in a climate subject to both natural and human induced forcings. In Chapter one, we employ a novel nonparametric probability density estimation method that allows for the characterization of nonlinear multivariant relationships among climatological variables. We develop and use this framework to quantify the multivariant relationships that exist between California wintertime temperature and precipitation as a function of naturally occurring low-frequency modes of variability to understand how they alter the probabilities for experiencing co-occurring extremes. Of the several modes studied, we find that a circumpolar Rossby wave of wave number 5 is uniquely capable of simultaneously driving both high temperatures and low precipitation thereby inducing or exacerbating antecedent drought conditions. Chapters two and three both employ two large ensembles of global climate model simulations. One ensemble is configured to represent the climate as it is today and the other configured to represent the best estimate of what the climate would have looked like in the absence of human interference. In chapter two we leverage the statistical power provided by the large ensembles to study the anthropogenic influence on the spatio-temporal characteristics of extreme hydrometeorological events across the continental U.S. from 1960-2018. We identify an anthropogenic signal at nearly every time scale considered and find that hydrometeorological events characteristic of the mean scale at approximately Clausius-Clapeyron (7\%K$^{-1}$), while extreme events representing the 99\textsuperscript{th} percentile are found to scale at nearly double that rate. In Chapter three, we use the same dataset to isolate and quantify the human contribution to the observed hydroclimate variability. We find that anthropogenic forcing has resulted in a large increase in western U.S. hydroclimate variability and volatility, a decrease predictability for any given ocean state, and to simultaneously permit an increased probability for both droughts and floods in California. Our results suggest that the outcomes of both the strong El Ni\~no of 2015/16 failing to drive the expected hydroclimate response in California and the following extremely wet winter of 2016/17 were both made more likely as a result of anthropogenic forcing.