As the electric power sector faces increasing demands from electrification, data center growth, and climate change, accurate long-term load forecasting has become essential for capacity expansion, transmission planning, and resource adequacy assessments. This presentation outlines the critical role of environmental data—particularly temperature and solar irradiance—in modeling hourly load variability. It emphasizes the importance of integrating historical observations, reanalysis datasets, and climate model projections to capture both natural variability and long-term climate trends. Various data sources and methodologies are reviewed, including statistical adjustments, dynamical modeling, and hybrid approaches such as Quantile Delta Mapping. The limitations of using Typical Meteorological Years (TMYs) are discussed, advocating instead for multi-year weather representations to better capture extreme and compound events. The presentation concludes by recommending a risk-aware, data-diverse approach tailored to the specific analytical context, ensuring more robust and actionable forecasts for future power system planning.