The climatology method is the forecasting approach that uses historical averages to predict future weather. It works by averaging weather data recorded over many years for a specific date or season, then using that average as the forecast. If you’re studying for an earth science class or just curious about how forecasts work, this is the method your search is pointing to.
How the Climatology Method Works
The concept is straightforward. To forecast weather for a particular day, you gather all the recorded data for that same calendar date across many previous years and calculate the average. If you wanted to predict the weather in New York City on July 4th, you would pull every recorded July 4th observation, then average the temperatures and precipitation totals. If those averages came out to a high of 87°F with 0.18 inches of rain, that becomes your forecast: 87°F and 0.18 inches of rain.
This works for any measurable weather variable: temperature, rainfall, humidity, wind speed. The key idea is that past patterns, when averaged together, give a reasonable baseline expectation for what a given day or month will look like.
What “Climate Normals” Actually Mean
The averages used in climatology forecasting aren’t just informal calculations. They follow a specific international standard. The World Meteorological Organization (WMO) requires member nations to compute 30-year averages of meteorological data, and NOAA (the National Oceanic and Atmospheric Administration) produces these for nearly 15,000 U.S. weather stations. The current standard period is 1991 to 2020.
These 30-year averages are called “climate normals.” They include annual, seasonal, monthly, daily, and even hourly statistics for temperature, precipitation, and other variables. NOAA updates these normals every 10 years, both to reflect changing climate patterns and to incorporate data from newer weather stations. When a weather report says temperatures will be “above normal” or “below normal,” it’s comparing the forecast against this 30-year average. The difference between what actually happens and the normal value is called an anomaly.
Strengths and Limitations
The climatology method is useful for long-range planning. If you need a general sense of what weather to expect weeks or months from now, when detailed atmospheric models lose their accuracy, averages give you a reliable baseline. Agriculture, energy planning, and forestry all depend on this kind of seasonal outlook. Farmers use climate averages to decide planting schedules, and energy companies use them to anticipate heating or cooling demand.
The obvious limitation is that climatology tells you nothing about what’s actually happening in the atmosphere right now. It can’t account for an incoming storm system or an unusual cold snap. For short-term forecasts (the next few days), it’s far less useful than methods that analyze current conditions. Research from the American Meteorological Society has shown that combining climatology with persistence forecasting, which assumes tomorrow’s weather will resemble today’s, produces more accurate results than either method alone.
Other Forecasting Methods That Use Averages
The climatology method isn’t the only approach that relies on averaging. Two others are worth knowing about.
Ensemble forecasting is the method used by modern weather prediction centers. Instead of running a single computer simulation of the atmosphere, forecasters run dozens of simulations, each starting with slightly different initial conditions to account for uncertainty in observations. The average of all these simulations, called the ensemble mean, smooths out the unpredictable noise in any single run. This is a fundamentally different use of averaging: rather than looking backward at historical data, it averages multiple possible futures to find the most likely outcome.
The analog method searches historical records for past days when atmospheric conditions closely resembled the current situation. Once a set of similar past days is identified, the weather outcomes from those days form a statistical distribution that serves as the forecast. This is more targeted than pure climatology because it filters for days with matching weather patterns rather than averaging every instance of a calendar date.
Why Climatology Still Matters
Even with supercomputers running sophisticated atmospheric models, the climatology method remains a benchmark. Meteorologists use it as a baseline to measure whether their advanced forecasts actually add skill. If a complex model can’t beat the simple 30-year average, it’s not providing useful information. Climate normals also anchor how we communicate weather: phrases like “10 degrees above average” or “drier than normal” only make sense because we have these long-term averages as a reference point. The method is simple by design, and that simplicity is exactly what makes it a foundational tool in meteorology.