When Austrian hydropower production plummeted by 44% in early 2025 due to reduced snowpack, it exposed a critical vulnerability: standard meteorological and climatological datasets systematically fail in mountain regions that hold untapped renewable potential. This perspectives paper evaluates emerging solutions to the Alpine energy-climate data gap, analyzing datasets from global reanalyses (ERA5, 31 km) to kilometre-scale Digital Twins (Climate DT, Extremes DT, 4.4 km), regional reanalyses (ARA, 2.5 km), and next-generation AI weather prediction models (AIFS, 31 km). The multi-resolution assessment reveals that no single dataset excels universally: coarse reanalyses provide essential climatologies but miss valley-scale processes, while Digital Twins resolve Alpine dynamics yet remain computationally demanding. Effective energy planning therefore requires strategic dataset combinations validated against energy-relevant indices such as population-weighted extremes, wind-gust return periods, and Alpine-adjusted storm thresholds. A key frontier is sub-hourly (10-15 min) temporal resolution to match grid-operation needs. Six evidence-based recommendations outline pathways for bridging spatial and temporal scales. As renewable deployment expands globally into complex terrain, the Alpine region offers transferable perspectives for tackling identical forecasting and climate analysis challenges in mountainous regions worldwide.