*From Kalshi and Polymarket to niche scientific platforms, traders are predicting the weather — and climate experts are debating the results.*
*Mary Hui, Eric Roston, and Joe Wertz for Bloomberg News*
A few days before Christmas, Howard Qin was scrutinizing weather forecasts on two laptops at home in Shanghai when he noticed prices of contracts for New York City snowfall creeping higher on the prediction market Kalshi. He checked the Times Square webcam for specks of white — affirmative.
The recent Stanford math graduate and lifelong weather buff had spent about $200 on predictions that total NYC snowfall that month would top various thresholds — for example, more than 2 inches. Now the value of his shares was rising.
There was just one wrinkle: Qin had a classical guitar recital to attend that night. “I’m not going to bail on my concert just to trade on this. That would be a little extreme,” he recalls thinking. He sold and netted $327.79 — a 57% gain and his biggest win yet.
That was 2024. Two years later, Qin, now 24, is still making small bets “just for fun,” including on Central Park snowfall during January’s megastorm (he won a few dollars). What’s changed is the sheer number of people placing bets on weather markets — and the amount of money flowing through them. Trading volume for the January snowstorm topped $6 million on Kalshi, one of the largest weather contracts ever traded on the rapidly growing platform.
That’s still tiny compared with Kalshi bets on sports and elections. But weather trades are gaining traction, drawing in casual participants, weather experts and AI-driven weather-tech firms testing their wares. As these markets grow, weather nerds and climate researchers are debating whether prediction markets can improve forecasts by aggregating knowledge — and, in turn, inform investment and policy — or whether they’re simply zero-sum games in which uninformed gamblers make (or lose) a quick buck.
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*From Kalshi and Polymarket to niche scientific platforms, traders are predicting the weather — and climate experts are debating the results.*
*Mary Hui, Eric Roston, and Joe Wertz for Bloomberg News*
A few days before Christmas, Howard Qin was scrutinizing weather forecasts on two laptops at home in Shanghai when he noticed prices of contracts for New York City snowfall creeping higher on the prediction market Kalshi. He checked the Times Square webcam for specks of white — affirmative.
The recent Stanford math graduate and lifelong weather buff had spent about $200 on predictions that total NYC snowfall that month would top various thresholds — for example, more than 2 inches. Now the value of his shares was rising.
There was just one wrinkle: Qin had a classical guitar recital to attend that night. “I’m not going to bail on my concert just to trade on this. That would be a little extreme,” he recalls thinking. He sold and netted $327.79 — a 57% gain and his biggest win yet.
That was 2024. Two years later, Qin, now 24, is still making small bets “just for fun,” including on Central Park snowfall during January’s megastorm (he won a few dollars). What’s changed is the sheer number of people placing bets on weather markets — and the amount of money flowing through them. Trading volume for the January snowstorm topped $6 million on Kalshi, one of the largest weather contracts ever traded on the rapidly growing platform.
That’s still tiny compared with Kalshi bets on sports and elections. But weather trades are gaining traction, drawing in casual participants, weather experts and AI-driven weather-tech firms testing their wares. As these markets grow, weather nerds and climate researchers are debating whether prediction markets can improve forecasts by aggregating knowledge — and, in turn, inform investment and policy — or whether they’re simply zero-sum games in which uninformed gamblers make (or lose) a quick buck.
[Read the full story here.](https://www.bloomberg.com/news/articles/2026-04-10/what-are-weather-prediction-markets-and-do-they-work?accessToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzb3VyY2UiOiJTdWJzY3JpYmVyR2lmdGVkQXJ0aWNsZSIsImlhdCI6MTc3NTk5NDMxNSwiZXhwIjoxNzc2NTk5MTE1LCJhcnRpY2xlSWQiOiJURDlLTzZLSUpISUQwMCIsImJjb25uZWN0SWQiOiJEMzU0MUJFQjhBQUY0QkUwQkFBOUQzNkI3QjlCRjI4OCJ9.pj8WzLfndfumu24gY0-37yeVNprG7UXqvkno4uxGNJU)