Wuhan's weather on May 17 is the subject of four linked prediction markets, each addressing a different temperature threshold. Rather than a single binary outcome, this structure allows traders to express views across a range of possible high temperatures—whether the daily maximum will land at or below 24°C, at 25°C, at 26°C, or at higher levels. This granular approach reveals not just what the trading community expects will happen, but the full spectrum of outcomes they consider plausible and how confident they are in each scenario. By examining prices across all four markets simultaneously, you can reconstruct the collective forecast embedded in prediction market activity. When traders allocate capital across multiple temperature bands, the resulting prices function as an implicit probability distribution. If most participants expect Wuhan's high to settle in the 25–26°C range, you'll observe that reflected in higher prices for those specific outcomes and lower prices for extreme scenarios like 24°C or above 27°C. The price gaps between adjacent temperature levels signal how the market community weighs marginal shifts in expected outcomes—tight clustering indicates stronger consensus, while wider spreads reflect genuine uncertainty. These grouped markets serve practical purposes for climate monitors tracking regional weather patterns, businesses managing weather-dependent operations, traders building probabilistic views, or anyone interested in how dispersed information aggregates into market-based forecasts. Real-time price movements capture shifting expectations as new weather data emerges, making this an efficient window into how markets continuously update their assessments of future conditions.