An hourly climate projection and renewable energy generation dataset for power system modeling in China.

As renewable energy deployment expands, power systems are increasingly sensitive to climate variability. Global Climate Models (GCMs) are commonly used to assess climate change impacts on future power systems. However, GCM outputs are typically only available at daily or coarser temporal resolutions, insufficient for the hourly granularity required by power system models, as both demand and renewable output show strong diurnal variations affecting system operation and planning. To address this, we present an hourly climate projection and renewable energy generation dataset for China, developed using an analog-based temporal downscaling method at a 0.5° spatial resolution. Our dataset ensures physically consistent meteorological variables and coherent daily statistics across multiple GCMs and socioeconomic pathways (SSPs), providing physically credible representations of plausible future climates rather than precise forecasts. Covering 2021-2060, it includes projections from five GCMs under four SSPs. The meteorology and renewable power dataset support studies on renewable energy potential, power system reliability, and energy transition pathways under future climate conditions, bridging the gap between climate projections and energy system modeling.