Panama City's high temperature on May 5, 2026, is the subject of seven closely linked prediction markets, each asking whether the daily high will reach a specific threshold—20°C, 21°C, 22°C, 23°C, or 24°C. These markets are bundled together because they all depend on the same underlying variable: the actual measured high temperature in Panama City that day. By examining how these markets are priced relative to one another, you can reconstruct the collective forecast of what participants expect the weather to be. The relationship between these prices is constrained by logic: if 20°C is priced at 80% probability, then 21°C must trade lower, since reaching 21°C implies 20°C was also reached. This internal coherence means that reading across all seven prices simultaneously reveals a full probability distribution—a complete picture of what the consensus view is, from the odds of a cool day to the odds of heat. The grouping is practical: instead of a single yes/no outcome, you're comparing the likelihoods of a spectrum of concrete, measurable results. Small price differences across adjacent temperature thresholds suggest the market sees those outcomes as nearly equally likely, while large gaps indicate sharp shifts in consensus. Whether you're interested in meteorology, testing your own forecast against market opinion, or simply exploring how conditional probabilities work in practice, these markets offer a transparent, real-time window into what a diverse group of participants believes will happen on May 5 in Panama City.