On April 27, 2026, Munich's weather will determine the outcome of four linked prediction markets that collectively map the range of expected high temperatures for that day. These markets—tracking whether the city's highest temperature will reach 12°C or below, or precisely 13°C, 14°C, or 15°C—represent a common prediction market pattern: when a real-world event has multiple plausible outcomes, related markets emerge to let forecasters express nuanced expectations. The probabilities across these four markets reveal what the collective market assigns as most likely for that day's peak temperature. By examining how probability distributes across the 12–15°C range, readers can infer the market's central temperature estimate: if the 14°C market shows the highest probability, that suggests forecasters expect Munich's high to cluster near that threshold. Lower probabilities on adjacent temperatures indicate more certainty about the specific outcome; high probabilities spread across multiple outcomes suggest wider uncertainty. This bundled view is particularly useful for understanding market-derived weather forecasts, since it avoids the binary framing of single prediction markets (e.g., "Will it exceed 14°C?") and instead shows the full distribution of expectations. You can compare these market prices against traditional meteorological forecasts for the same day to see where professional predictions and market forecasts align or diverge. The prices below represent real-time consensus estimates from thousands of independent forecasters worldwide.