Climate-State-Conditioned Compound Weather-to-Grid Scenario Generation for Sustainable Long-Term Distribution Planning

Long-term distribution planning requires weather sequences that capture not only changes in temperature and precipitation, but also the dependence and persistence among heat, humidity, wind, solar radiation, and rainfall. This paper develops multivariate weather scenario generation for Shaanxi Province, China, using daily data from six NEX-GDDP-CMIP6 models for 1985–2014 and 2031–2060 under SSP2-4.5 and SSP5-8.5 at four locations. Two generators are compared: a vector autoregressive model with seven-day residual blocks, and a season-conditioned multivariate nearest-neighbor analog. The comparison leaves out complete five-year periods within 24 climate-model–location units and tests whether the future-minus-historical changes in 16 weather and compound-event indices are preserved. The two methods each obtain the lower overall loss in 12 units. The vector autoregressive method better preserves most marginal and correlation signals, whereas the nearest-neighbor method better preserves RX3day, hot–dry–low-wind days, dry-spell duration, and three-day compound stress. Both are retained to generate 960 30-year paths, which drive a fixed IEEE 33-node resilience example showing that generator differences propagate to demand, renewable availability, repair duration, and unserved energy. The resulting conditional scenario sets provide an auditable weather basis for climate-resilient and sustainable long-term planning of distribution systems, which is a prerequisite for a sustainable energy transition under a changing climate.