A 11-year prospective experiment has found that several operational-style earthquake models can remain statistically consistent with real California seismicity over a defined 24-hour forecast window. The study evaluated 27 models across 51,625 next-day forecasts and 597 observed earthquakes of magnitude Mw 3.95 or greater.
That is evidence that probabilistic risk estimates can be useful and remain calibrated in real time. It is not evidence that scientists can name the exact time, location or size of the next earthquake.
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What the California experiment tested
The peer-reviewed study, led by José A. Bayona and published in Nature Communications, was conducted through the Collaboratory for the Study of Earthquake Predictability (CSEP). Forecasts were issued between August 2007 and August 2018, before the earthquakes used to evaluate them occurred.
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This distinction matters. A model can appear impressive when it is fitted and tested on the same historical record. Prospective testing removes that particular hindsight advantage and asks whether the system continues to perform when it must make repeated real-time forecasts.
The results in numbers
| Measure | Result | What it means |
|---|---|---|
| Forecasting models | 27 | Different earthquake-forecasting systems evaluated in the same experiment |
| Next-day forecasts | 51,625 | 24-hour forecasts issued at midnight UTC during the study period |
| Observed earthquakes | 597 | California earthquakes of Mw 3.95 or greater used as evaluation events |
| Event-count consistency | Approximately 95% of models | Matched the observed number of earthquakes closely enough under the study’s statistical tests |
| Location consistency | About two-thirds of models | Were consistent with where earthquakes occurred |
| Magnitude consistency | Approximately 95% of models | Were consistent with the observed magnitude distribution |
The percentages are reported by the Statewide California Earthquake Center in 2026 and describe statistical consistency, not a guarantee that an individual forecast was correct. A model can match the long-run rate or magnitude distribution while still missing a particular earthquake’s exact location or timing.
Why “prospective” is stronger evidence
Forecasts came before the target events
For every 24-hour window, the model produced its forecast using information available before the window opened. The target earthquakes were collected afterward. This sequence prevents researchers from quietly changing the forecast rules to accommodate known events.
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One evaluation framework covered all models
CSEP applied community-vetted statistical methods to compare forecasts with observations. Using a common framework makes it easier to distinguish a genuinely reliable model from one that merely benefits from a favorable selection of events or a customized scoring method.
Long repetition exposes drift
More than 51,000 forecast windows span ordinary quiet periods as well as bursts of activity. Sustained consistency over that many windows is more informative about operational use than a short retrospective test, although it still cannot establish perfect performance in every future sequence.
What the findings say about earthquake prediction
The study validates a narrower claim than “scientists can predict the next earthquake.” Probabilistic forecasting estimates how likely earthquakes in specified magnitude, location and time ranges are over a stated period. It does not identify a guaranteed event.
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Bayona summarized the boundary clearly: “No scientific method can currently predict exactly when and where the next earthquake will occur. However, probabilistic forecasting can provide useful information about where, when and how large earthquakes are more likely over a defined period.”
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Consequently, the experiment does not show that the next major California earthquake can be announced in advance, nor that any one of the 597 events was individually predictable. It shows that some models’ probability distributions stayed compatible with what actually happened when tested prospectively.
Which models look most useful for real-time operations?
The results were not a blanket endorsement of all 27 systems. The authors reported sustained consistency for select versions of established operational-type models. “Operational” here refers to systems designed to produce continuing forecasts for use by agencies or other decision-makers, rather than one-off academic demonstrations.
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A meaningful comparison should examine all of the following dimensions:
- Event counts: whether the forecasted number of earthquakes is consistent with the observed number.
- Spatial distribution: whether elevated probabilities occur in the areas where earthquakes are actually observed.
- Magnitude distribution: whether the balance of smaller and larger events matches observations.
- Forecast horizon and threshold: the tested window was 24 hours and the evaluated catalog began at Mw 3.95 or greater; performance outside those limits is not established by this experiment.
- Operational design: whether the system can run repeatedly with newly available seismic data.
- Reproducibility: whether code, benchmark data, documentation and evaluation procedures are available for independent testing.
These criteria prevent a model from being called “accurate” on the basis of only one dimension. A system may reproduce event rates while providing weak geographic information, or match locations while misrepresenting the magnitude distribution.
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Better calibration of public risk estimates
Emergency-management and scientific agencies can use long prospective records to check whether a forecasting system remains calibrated as conditions change. The appropriate output is a probability tied to a time interval, geographic area and magnitude range—not a deterministic warning.
A benchmark for new models
The project released software tools, benchmark data and documentation. Researchers can use those materials to run new forecasting approaches through comparable tests, making it easier to identify improvements without changing the rules for each model.
A path toward international operational systems
The authors’ recommendations are intended to support development of operational earthquake-forecasting systems worldwide. The California experiment provides a template for repeated, transparent evaluation, but results from California should not automatically be assumed to apply unchanged to another tectonic setting.
How forecast messages should be communicated
A responsible forecast should state four things together: the probability being reported, the geographic area, the time window and the magnitude range. For this study, the relevant window was the next 24 hours and the event threshold was Mw 3.95 or higher.
Public language should avoid claims such as “an earthquake will strike tomorrow” or “the next big one has been predicted.” Such wording converts a probabilistic estimate into a promise the models do not make. A more faithful description is that the system estimates how earthquake likelihood is distributed across possible places and magnitudes during a specified period.
Limits readers should keep in mind
- The experiment covers California, August 2007 through August 2018, and earthquakes at or above Mw 3.95; it does not establish performance for every magnitude, region or forecast horizon.
- Approximately 95% consistency for counts or magnitudes does not mean 95% of individual earthquakes were predicted.
- About two-thirds location consistency leaves substantial room for missed or misplaced spatial probabilities.
- Only select versions of established operational-type models showed sustained consistency; the aggregate result does not rank every model as equally reliable.
- Probabilistic forecasts support risk decisions, but they cannot provide an exact date, epicenter and magnitude for a future earthquake.
What this study proves—and what it does not
Nearly 600 observed earthquakes gave researchers a substantial real-world test of next-day seismic-risk models. Across 51,625 forecasts, many systems remained statistically consistent with observed event counts and magnitudes, while fewer achieved that standard for locations. The strongest evidence is for using carefully evaluated probabilistic systems as ongoing risk information, accompanied by transparent methods and clearly stated limits.
The central conclusion remains deliberately modest: forecasting models can be useful without being crystal balls. Their value lies in calibrated probabilities over defined windows, not in naming the next earthquake before it happens.
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