On April 28, 2026, Munich's weather will reach a peak temperature, and three linked prediction markets help forecast precisely where that high will fall. These markets pose binary questions—will the highest temperature be 12°C? 13°C? 14°C?—and together they reveal something more nuanced than any single forecast. When examined as a group, their prices tell you where the collective market believes the temperature will peak. If the 12°C market trades high and others trade low, the crowd expects temperatures below that level. If 14°C dominates pricing, consensus leans warmer. This clustering of related predictions demonstrates how modern forecast markets work: precise binary boundaries let traders express granular views about uncertain future conditions with clarity. For weather analysts and prediction market enthusiasts, these temperature outcomes matter because they test both meteorological forecasting accuracy and how markets aggregate distributed knowledge. Weather itself is inherently uncertain—professional meteorologists offer probability ranges rather than point predictions—and these markets quantify that uncertainty in real time through price. The prices you see reflect thousands of trades by participants with varying expertise: some follow detailed meteorological models, others observe seasonal and geographic patterns, still others trade on general intuition combined with recent forecasts. What emerges is a collective probability estimate for each temperature outcome. By studying how these three markets price relative to one another, you gain insight into the market's confidence distribution across the temperature range. High prices across multiple outcomes suggest genuine uncertainty; concentrated price mass on one outcome signals strong consensus. These markets function as a real-time public gauge of temperature expectations, continuously updated as traders adjust positions based on new meteorological forecasts and incoming weather data.