On April 28, 2026, Denver's weather becomes the focus of four interconnected prediction markets, each assessing the probability that the city's highest temperature will fall within a specific range. These markets are grouped together because they track the same underlying event—Denver's daily high—but segment the outcome space into distinct temperature bands. This structure reveals something essential about weather forecasting: rather than a single yes-or-no proposition, you see a spectrum of probabilities distributed across adjacent ranges, from the coldest scenario at 32–33°F through milder conditions approaching 49°F. The price displayed for each market represents the real-time collective probability that Denver's high will land within that range. Higher prices indicate stronger consensus that an outcome is likely; lower prices suggest traders assess it as less probable. By reading all four markets together, you construct a complete picture of the weather forecast embedded in trading activity. Notice that these probabilities will not sum to 100 percent—prediction markets exist for other temperature ranges not displayed here—reflecting the full distribution of possible outcomes. This format is particularly valuable for understanding not just whether conditions will be cold or mild, but precisely where traders expect the day's high to settle. As updated weather models and forecasts emerge, prices across these related markets shift in concert, offering real-time insight into how collective sentiment evolves. For those tracking weather prediction accuracy or exploring how group intelligence aggregates forecasts, these bundled markets provide a transparent, data-driven window into what thousands of independent forecasters collectively believe Denver's weather will be.