China's installed renewable capacity overtook coal-fired capacity at the end of 2024, and from 2025 renewable generation is being brought fully into the market with prices set by trading, so that spot-price volatility, in the form of zero, negative and spike prices, has become routine. Day-ahead and real-time price forecasting is therefore a shared need for retailer bidding, storage arbitrage and renewable-revenue assessment, yet the open data underpinning such research remain weak: existing benchmarks come mostly from mature European and U.S. markets, and most do not record whether each column was actually available at bidding time, which invites the accidental use of future information in back-tests. Using Guangdong (a first-batch pilot, the largest spot market by traded energy, renewable-heavy, and located on a coastal typhoon corridor that makes prices weather-sensitive) as a representative Chinese market, we release a dataset for day-ahead price forecasting. On a unified Beijing-time hourly grid it organises 17 tables and 152 documented columns across market, weather, external-driver and news domains, targeting province-wide day-ahead and real-time settlement prices and shipping official day-ahead boundary forecasts, 24/48-hour-ahead weather forecasts for 21 cities, nodal-price signals, international fuel and carbon prices, typhoon proximity and a large-language-model news-sentiment signal. Its design has three distinctive features. First, every column is labelled with its real availability at bidding time, with as-of-bid columns kept separate from ex-post and settlement columns, so that an honest day-ahead forecast provably reads no future information. Second, the multi-source coverage spans supply and demand, constraints, cost and sentiment. Third, the pipeline is reproducible end to end, with per-source provenance, raw-response checksums, and an accompanying direction-prediction benchmark task with a neutral baseline. The dataset provides a time-aligned, caliber-transparent and reproducible empirical basis for spot-price-forecasting research in China.