5 real US droughts, reconstructed from nothing but soil physics

One of the most convincing checks a weather model can pass is finding real historical events it was never told to look for. Anyone can build a model that's internally consistent — that doesn't make it right.

The method

For each of the 12 covered cities, we ranked every backtest year from 1940 to 2025 purely by the model's own soil-moisture-deficit numbers, then checked the driest years against real, documented, named US droughts. The model never saw a list of drought years; it only ever computed a water balance from temperature, rainfall, and evapotranspiration.

What came back

• The Northeast/Mid-Atlantic drought of record, 1961-1966 (New England's worst on record, with real NYC and NJ water restrictions) — hit Boston (1965), New York City (1966, 1964), Washington DC (1966, 1964, 1963), and Pittsburgh (1965, 1963).
• The 1950s Midwest drought — 1953 was St. Louis's literal driest year on record — hit St. Louis (1953, 1952), Chicago (1953), and Detroit (1952, 1953).
• The 1988-1990 North American drought, one of the costliest in US history — hit Minneapolis (1988), Detroit (1988), and Pittsburgh (1988).
• The 2012 US drought, the second most widespread on record — hit five of the twelve cities: Chicago, Minneapolis, Denver, St. Louis, and Salt Lake City.
• The Great Western Snow Drought of 2015 (drought.gov's own name for it — a warm winter, low-snowpack event specific to the Pacific Northwest) — hit Seattle and Portland in the exact same year.

Every one of the 12 cities landed inside a real drought window at least once. Several hit three or four of their five driest years — Pittsburgh among them, at three of five.

How strong a signal that actually is

Across all 60 driest-year entries (12 cities × their top 5), 25 — 42% — fall inside one of these named drought windows. Those windows together span only about 14 of the 86 backtest years, roughly 16% of the record. A random match rate would land close to 16%; the model landed at 42%, close to 2.6x over-representation.

Several of these are short windows (3-6 years) rather than single-year exact hits — a real, specific signal, not a vague "it was probably dry" impression.

What it doesn't mean

This doesn't make the model infallible — weather forecasting and biology both carry real uncertainty. But every number here earned its place by being checked against real, dated events, not by sounding reasonable in a spreadsheet.

Read the full validation approach on how it works, or sign up and let it do the watching for your own city.