The quality of modern life is intrinsically tied to the development and maintenance of infrastructure systems. Modern energy and electricity infrastructure systems have high-reliability requirements, with people expecting power at the flip of a switch. The complex market structure and public-private partnerships at multiple levels in power generation and transmission systems make ensuring high reliability even more difficult. The 21st century brings with it multiple challenges and opportunities within these sectors. A large portion of the infrastructure fleet, like dams and fossil fuel generation plants, is old and needs replacement. Further, the decarbonization of the power sector is poised to result in the inclusion of large amounts of variable renewable energy sources, thereby introducing stochasticity in supply. The research presented in this dissertation seeks to assess and improve energy infrastructure resilience against regional spatiotemporal climate risk in the face of the upcoming decarbonization of the power sector. This dissertation seeks to develop our understanding of climate risk to energy infrastructure systems at a regional level. The analysis will be focused on the identification of organized modes of climate variability that lead to space-time clustering of risk.These investigations are accompanied by specific case studies in the contiguous United States and are applicable to electricity grids and river basins. Overall, I will focus on the ability to simulate and predict extreme climate events which pose reliability and failure risks to energy infrastructure systems. Since such events are rare, I propose methods that establish event excedance probabilities accounting for their underlying uncertainties. In chapter I, I present a novel statistical simulation model that can produce realistic, synthetic realizations of hydroclimatic fields across a large region. This k-nearest neighbor-based space-time simulator can be applied to single or multiple hydroclimatic fields across a large domain. The algorithm facilitates the estimation of the probability of extreme events that are not well represented in relatively short observational records. I apply this algorithm to wind and solar fields across the Texas Interconnection. Many regions plan to integrate more wind and solar generation into the energy grid, increasing power supply variability that can pose risks of under-supply. This simulation tool facilitates the estimation of the probability of regional wind and solar energy “droughts” and hence allows for the estimation of the storage needed to achieve desired supply-side reliability. In chapter II, I present a clustering based variant of the simulator developed in chapter I. I show how the algorithm developed in chapter I is a special case of a general class of algorithms. In Chapter II, I generalize the algorithm by introducing clustering on the neighbor likelihoods, thereby allowing for the identification of sub-regions with different state-space evolution characteristics. This allows for the application of the generalized algorithm to cases with greater heterogeneity, for example, increased temporal resolution. The clustering based k-nearest neighbor space-time simulator was developed to generate synthetic simulations of wind-solar data at an hourly timescale. I present an application of this algorithm to hourly wind-solar data across the Texas Interconnection. The application of this algorithm to estimate the underlying uncertainty and risk faced by power producers in entering short-term power supply contracts is demonstrated. In chapter III, I present a retrospective analysis of inferred energy demand trends across the contiguous United States. Future net zero scenarios generally require replacing all fossil-fuel heating with electric heat, thereby precipitating higher electricity peak loads during winter. Assuming 100% penetration of efficient electric space-heating and cooling, this chapter carries out a spatially explicit trend analysis of temperature-based proxies of electricity demand over the past 70 years. As expected, annual mean heating and cooling demand decreases and increases over most of the contiguous US, respectively. Peak thermal load is generally dominated by heating, showing large inter-annual and decadal variability, thus far not displaying statistically significant decreasing trends. In the south, the peak cooling demand has started to dominate the peak demand, but the possibility of an occasional high peak heating demand can not be discounted. Conversely, in the north, the average thermal loads are declining while the peak thermal loads are not. This points to the need for an improved pre-season forecast of peak winter heating loads. In chapter IV, I present a method for the diagnosis of low-frequency climate variability from multi-site data, which leads to spatiotemporal clustering of flooding risk at a regional level. Disruptions to energy infrastructure systems are often caused due to flooding, and the characterization of climate risk to energy infrastructure due to flooding is explored in this context. The approach is demonstrated using the Ohio River Basin as a case study. I show that the dominant timescales of flood risk within the Ohio River Basin are in the interannual (6-7 years), decadal (11-12 years), and long-term (secular) scales, with different sub-regions responding to different climate forcings. These leading modes are associated with El-Nino Southern Oscillation and secular trends. Further, the secular trend points to an east-to-west shift in flood incidence and changes in the storm track, which are consistent with certain climate change projections. Overall, the results point to the presence of compound climate risk inherent at regional levels, with the low-frequency climate variability translating into periods of increased and decreased flood risk, which all the stakeholders should consider.