Every temperature prediction market on Kalshi settles against the National Weather Service Daily Climate Report. That makes the NWS forecast the single most important data point in weather trading. And like any forecast, NWS forecasts have biases that vary by city, by season, and by whether you're looking at daily highs or daily lows.
Understanding these trends is one of the most fundamental edges available in temperature markets.
Forecast bias is the average direction a forecast misses over time. If NWS forecasts the high temperature for a city and the actual recorded temperature consistently comes in warmer, that city has a cold bias — NWS is systematically underforecasting. If actuals consistently come in cooler than forecast, it has a warm bias.
This is different from forecast accuracy. A forecast can be accurate on average — the errors cancel out — but still have bias in specific conditions. Or it can have low bias but high error, meaning it's not systematically off in one direction, but individual forecasts miss by a lot in both directions.
For weather betting, bias matters more than raw accuracy. If you know NWS consistently underforecasts by half a degree for Miami, you can adjust your view accordingly.
NWS forecast bias varies significantly across cities. Some cities see NWS miss by a fraction of a degree on average. Others see consistent misses of 1–2°F in the same direction. Treating all cities the same is leaving information on the table. A trader who tracks per-city bias over time develops a view of each city that most market participants don't have.
One of the less obvious patterns is that NWS forecast bias can run in opposite directions for daily highs and daily lows in the same city. NWS might consistently underforecast the daily high — actuals come in warmer — while overforecasting the daily low — actuals come in colder. What this means is that NWS tends to compress the daily temperature range: it doesn't predict enough spread between the high and the low.
Forecast bias is not static. It shifts with the seasons. A city that NWS forecasts well in summer might have significant bias in winter when weather patterns are more dynamic. Spring and fall transition periods can introduce temporary biases as the atmospheric regime changes. This means tracking bias is an ongoing process, not a one-time analysis.
Here is how bias translates into actual trading decisions. Say NWS forecasts a high of 78°F for a city where your tracking shows a consistent cold bias of -1°F — meaning NWS underforecasts by about a degree. Your bias-adjusted expectation is closer to 79°F. The market may be pricing the 77–78 bracket as the favorite because it contains the headline NWS number. But your adjusted view points to the 79–80 bracket as more likely.
Over many bets, that slight edge compounds. This doesn't mean 79–80 is a guaranteed winner. Temperature forecasts have uncertainty regardless of bias correction. But it means the 79–80 bracket might be slightly underpriced relative to its true probability.
Bias is a long-term average. It tells you the direction NWS tends to miss for a city, but it doesn't tell you what will happen on any specific day. Bias improves your baseline expectation; it doesn't eliminate uncertainty. Traders who over-rely on bias without considering other factors can get caught when the specific day doesn't match the long-term average.
The simplest way to start tracking forecast bias is to record the NWS forecast and the actual settlement temperature for the cities you trade every day. Over weeks and months, patterns emerge that are genuinely useful. Weather Edge Finder automates this process, tracking NWS accuracy and per-city bias across all 20 Kalshi cities continuously.