The title asks for the lowest temperature in Seoul, but the rules explicitly state the resolution source is Incheon Intl Airport (RKSI). Incheon is coastal and the airport is on an island, meaning its temperatures often differ significantly from inland Seoul. This is a highly misleading geographical trap. Additionally, the data resolves to whole degrees Celsius.

Predicting the exact count of global earthquakes of magnitude 6.5 or higher within a specific single week is highly niche. Unless one is a seismologist or a disaster-focused trader, ordinary people rarely ponder such highly random, short-term natural phenomena.

April 30, 2026 - May 3, 2026, the price of option 1 fell from 49.5c to 25.5c, option 2 from 33c to 17c, option 3 from 33c to 5.5c, and option 4 crashed from 32.5c to 2.25c. This was due to severe early mispricing and market inefficiency (the sum of Yes probabilities initially far exceeded 100%). Prices have since corrected towards theoretical probabilities based on the Poisson distribution as rational traders corrected the overvaluations.

Weather prediction markets are somewhat niche. While predicting the exact daily high in a specific city isn't a mainstream focus for most people, it is grounded in meteorology and not an extremely absurd or novelty question.

Moderate risk exists. The rules state resolution is based on initial data, ignoring later revisions. The major trap is the contingency clause: if NASA fails to publish data by July 1, 2026, it resolves to the lowest bracket. A technical delay or government shutdown could cause an unexpected resolution disconnected from actual temperatures.

Moderately exotic. While climate change is a mainstream topic, predicting the exact bracket of the global land-ocean temperature anomaly for a single month is highly niche. It appeals primarily to data geeks and meteorology followers rather than the general public.

Predicting the exact highest daily temperature of a specific city is somewhat niche in prediction markets. Ordinary people do not usually guess the precise temperature value for a random day, though weather betting itself is not completely unheard of.

There is a moderate divergence. Some mainstream weather platforms (like Wunderground and Google's general forecast) predict that Jiaozhou (where ZSQD is located) could reach highs of 26°C or 27°C on May 5, while the market assigns the highest probabilities to 23°C and 24°C. This discrepancy likely stems from traders mistakenly referencing cooler coastal temperature forecasts for Qingdao city rather than the specific inland airport location, which is typically warmer.

Weather prediction is a standard vertical in prediction markets. While not as universally mainstream as political elections or macroeconomic data, forecasting the exact lowest temperature in a specific city on a given day is a moderately typical question.

While weather forecasting markets are relatively common on specialized prediction platforms (e.g., Kalshi), betting on the exact precipitation bracket for a specific location in a given month remains a somewhat niche and novel topic for the general public.

Predicting the exact precipitation in millimeters for a specific city in a given month is a relatively niche weather derivative market. While not completely bizarre, it is not a topic the general public naturally contemplates.