São Paulo's weather on May 17, 2026 is the focus of this cluster of five linked prediction markets. Each market represents a specific temperature outcome—from a cool 13°C or below, up through 15°C, 16°C, 17°C, and 18°C highs. Together, these outcomes form a probability distribution that reveals what the crowd expects the city's highest temperature will be on that date. Prediction markets like these aggregate dispersed information from weather experts, climate analysts, and informed observers into real-time probability assessments. The prices you see below—expressed as percentages—represent the collective expectation of traders who put capital behind their forecasts. Reading these markets together tells a story: if the 16°C market trades at 35% and the 17°C market at 28%, you can infer that most participants expect a temperature somewhere in that range, with higher confidence in the lower end. Notably, the 18°C outcome captures the upper bound of this cluster; if it trades significantly lower than adjacent outcomes, it signals consensus that temperatures are unlikely to climb that high. These markets are useful for more than curiosity—meteorologists, urban planners, event organizers, and traders monitoring seasonal patterns all watch real-time probability signals as decision inputs. As you explore these prices, watch how they move in response to new forecast data, satellite imagery, or atmospheric models released by weather agencies. The spreads between outcomes reveal where uncertainty lies: tight spreads suggest high confidence, while wide gaps indicate divided opinion among market participants.