Chengdu's weather on May 5 has become a focus of prediction market activity, with multiple outcomes tracking the city's maximum temperature across a narrow, sequential range. The four markets here—covering whether the highest temperature will be 23°C or below, 24°C, 25°C, or 26°C—form a complete picture of early-May conditions in this inland Chinese city. Together, they allow observers to map precise market expectations about where the day's peak temperature will land. When examining these markets as a group, several important patterns emerge. The relative prices across the four outcomes reveal how the crowd distributes confidence across each temperature band. A clustered price distribution often signals genuine uncertainty about the exact threshold, while a sharp concentration in one or two outcomes indicates stronger directional consensus. Higher prices generally correspond to outcomes the market views as more probable; lower prices reflect lower perceived likelihood. The price gaps between outcomes also carry information: if the ≤23°C market trades at 35¢ while the 26°C market sits at 15¢, the 20-cent spread reflects where the market expects the temperature to land within that range. For those new to prediction markets, this structure offers insight into how markets price related sequential events. Temperature outcomes are mutually exclusive and exhaustive—exactly one will occur on May 5. This makes comparative analysis more intuitive: all four prices should sum near $1.00 in an efficient market, providing a useful consistency check. Chengdu's subtropical inland climate typically brings warm spring weather in early May, though regional patterns introduce variability. Prediction markets consolidate weather models, historical data, and real-time conditions into continuously updated prices. By tracking these four outcomes together, observers gain a transparent, crowdsourced forecast grounded in market incentives.