Hydrological extremes, including both extreme precipitation events and droughts, have profound impacts on human life, health, and socioeconomic well-being. U.S. hydrological extremes are dynamically connected to large-scale meteorological patterns (LMPs) (e.g., atmospheric blocking events, cutoff-low systems, and cyclones/anticyclones) and planetary-scale climate modes (PCMs) (e.g., El Niño Southern Oscillation and Pacific Decadal Oscillation). This dissertation applies both statistical and dynamical methods to gain a process-level understanding of the dynamics, variability, and model representations of U.S. hydrological extremes during boreal warm seasons. The first part of the work investigates the large-scale organization and modes of variability of U.S. hydrological extremes. The results in this part 1) demonstrate the connections among hydrological extremes in the United States, LMPs, and PCMs, 2) highlight existing shortcomings in climate model representations of spatial pattern, trends, and variability of regional hydrological extremes, and 3) illustrate the importance of properly simulating the observed spectrum of LMPs in effectively representing hydrological extremes. The second part of this study examines the dynamics of summer hydroclimate over northern midlatitudes, especially those related to U.S. hydrological extremes. The results in this part 1) suggest the existence of an important dynamical connection between the hydrological cycles of East Asia and North America, 2) highlight the importance of atmospheric nonmodal instability in the excitation, growth, and dissipation of atmospheric disturbances modulating the summer hydroclimate in northern mid-latitudes, and 3) demonstrates the significance of scale-interaction processes that inter-connect local hydrological extremes, large-scale atmospheric disturbances, and summer background flow. This study will help improve our understanding of the variability and changes of hydrological extremes in the past several decades, provide a general assessment of the performance of global climate models in simulating these extremes, and offer new insights on identifying the dynamical origins of potential model biases in representing U.S. hydrological extremes.