Power system planning tools require hourly weather data to capture the variable conditions that influence electricity supply and demand (e.g., temperature, wind, and solar). It is well-established that using current or historical weather conditions is insufficient for planning a resilient system under climate change and that forward-looking data are needed when conducting energy transition studies through 2050. However, nearly all global climate model (GCM) projections are limited to daily temporal resolution, presenting a data gap that must be addressed to incorporate climate change projections directly into existing commercial tools for power system modeling. This paper presents an innovative approach to create hourly weather timeseries for future climates. A monthly quantile delta mapping technique is used to produce realistic hourly weather data for a future climate by adding the monthly climate change signal projected by climate models to historical weather data. This method preserves important, real-world characteristics from the historical record that are otherwise missing from climate model output, such as locationally specific extremes which can be missing from coarse climate projections, local natural variability which may not be well represented in the climate models, and important joint correlations among physically linked variables such as wind, solar, and temperature. This approach has many potential applications in the power sector, including for capacity expansion and production cost modeling where select hourly timeseries are used for complex optimizations or simulations, as well as for resource adequacy assessments that evaluate large samples of realizations to identify possible extremes for stress-testing a future year of interest.