On May 5, 2026, this event aggregates four closely related prediction markets that all focus on Munich's highest temperature for that day. Rather than asking a single question, these markets break down the forecast into specific temperature thresholds: 15°C, 16°C, 17°C, and 18°C. These markets are grouped together because they are fundamentally interconnected. If Munich's highest temperature actually reaches 18°C, then the markets predicting 15°C, 16°C, and 17°C would also resolve favorably—they all forecast that the temperature will at least reach their respective levels. Similarly, if the day peaks at 16°C, only the 15°C and 16°C markets would resolve affirmatively. This nested structure means the prices across all four markets tell a coherent story about expected weather conditions. When reading the prices below, pay attention to the spreads between adjacent temperature levels. A narrow gap between the 17°C and 18°C markets suggests the community views those outcomes as roughly equally likely. A wide gap, by contrast, signals consensus that the temperature is unlikely to reach the higher threshold. By examining the full range of implied probabilities, you can see what most participants expect: Will Munich have a cool spring day, or will it warm into the upper range? The collective price signals across these four markets represent thousands of individual forecasts about Central European weather, aggregated into a single probability distribution. This kind of granular temperature forecasting demonstrates how prediction markets can turn distributed knowledge into actionable insights about specific real-world outcomes.