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There is no universal winner. GenCast is the strongest fit when you need probabilities, uncertainty, extremes, or planning as far as 15 days. GraphCast is the clearest fast, deterministic reference when you want one global forecast out to 10 days. Aurora is the broadest foundation-model option when hourly output, many atmospheric variables, transfer learning, or ensemble extensions matter more than a single apples-to-apples benchmark.
The headline accuracy percentages cannot be merged into one league table. Google DeepMind and Microsoft Research evaluated different model versions against different ECMWF baselines, target sets, lead times, and verification designs. The useful question is therefore not “Which number is largest?” but “What forecast decision are you trying to make?”
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Quick verdict
| Need | Best fit | Reason |
|---|---|---|
| One fast global forecast to 10 days | GraphCast | Deterministic output, a widely cited open reference implementation, and reported inference in less than a minute on one Google TPU v4. |
| Probabilities, uncertainty, extremes, or planning to 15 days | GenCast | Generates 50 or more possible trajectories with a diffusion model instead of presenting only one forecast line. |
| Many variables, hourly output, transfer, or fine-tuning | Aurora | Its foundation-model design and Aurora 1.5 capabilities target broader atmospheric tasks, expanded variables, and ensemble forecasting. |
| A strictly comparable ranking | None | The published results use different baselines, test targets, model versions, and verification setups. |
What problem does each model solve?
GraphCast: a fast deterministic forecast
GraphCast is a deterministic global medium-range weather model. It produces one best-estimate trajectory rather than a distribution of alternatives. The headline system forecasts up to 10 days ahead on a 0.25-degree grid and uses two recent atmospheric states as input.
That format is useful when an application needs a single coherent forecast field: a map for a dashboard, a baseline for a research experiment, or a quick input to a downstream model. Google DeepMind reports that GraphCast outperformed ECMWF’s deterministic HRES on 89.3% of 2,760 evaluated target variables and lead times. That is a conditional benchmark result, not a claim that every GraphCast forecast is better than every ECMWF forecast.
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GraphCast’s practical attraction is speed. Google DeepMind says a 10-day forecast takes less than a minute on one Google TPU v4, compared with hours for conventional supercomputer forecasting. The timing is for that hardware and workload; it should not be read as a promise of sub-minute performance on an ordinary laptop or an arbitrary cloud accelerator.
GenCast: an ensemble for risk-aware decisions
GenCast is probabilistic. Instead of issuing one track, it uses a diffusion model to generate an ensemble of 50 or more possible weather trajectories at 0.25-degree resolution, extending to 15 days. The result is a distribution that can express how tightly clustered or widely spread the plausible outcomes are.
This distinction matters for decisions such as renewable-power scheduling, flood preparation, supply-chain planning, and any operation where the cost of a miss is not symmetric. A deterministic forecast might say that a wind farm will see a particular wind speed. An ensemble can support questions such as “What is the probability that wind falls below the level needed to meet tomorrow’s schedule?” or “How wide is the range of plausible rainfall totals?”
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →In Google’s published evaluation against the ECMWF ENS ensemble, GenCast was more accurate on 97.2% of 1,320 targets and on 99.8% of targets for lead times beyond 36 hours. Those percentages describe that comparison’s targets and verification method; they do not measure GenCast directly against GraphCast or Aurora.
One Google Cloud TPU v5 can generate a 15-day ensemble forecast in eight minutes, according to the same published description. That is a useful indication of throughput for a specialized accelerator, not a general consumer-hardware requirement or guarantee.
Aurora: a broader atmospheric foundation model
Aurora is Microsoft’s atmospheric foundation model. Its original research compared the system with GraphCast and ECMWF IFS-HRES after pretraining on diverse data sources. The foundation-model approach is intended to transfer across atmospheric tasks instead of being limited to one fixed forecast configuration.
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The current Aurora 1.5 project page describes 22 additional forecast variables, hourly resolution, and probabilistic ensemble forecasting. Microsoft reports that Aurora 1.5 ensembles outperform ECMWF’s ensemble on 88.9% of evaluated targets for days 1–10. Because this is a different version and evaluation from the GraphCast and GenCast figures, it belongs in a separate evidence column rather than a combined ranking.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAurora is the most natural candidate when the application needs broader variable coverage, hourly products, or a model that can be adapted to related atmospheric tasks. It is less straightforward to summarize as “the best model” because its value includes breadth and transfer capability, not only one deterministic score.
Deterministic versus probabilistic forecasting
The GraphCast–GenCast distinction is primarily about the question being answered. Deterministic forecasting returns one trajectory. Probabilistic forecasting returns multiple plausible trajectories and lets the user estimate risk from their spread.
When one trajectory is enough
- A visualization needs one clear global map.
- A research project requires a fast, reproducible baseline.
- A downstream process has been designed around one best estimate and has its own uncertainty handling.
- The decision is low consequence and does not justify ensemble computation.
GraphCast’s deterministic output is easier to store, display, and compare. It also avoids the extra interpretation required when dozens of scenarios diverge.
When a distribution is essential
- Different outcomes have materially different costs.
- Thresholds matter, such as the chance of exceeding a rainfall, wind, temperature, or demand limit.
- Planning extends beyond the point where small initial-condition errors can produce widely separated tracks.
- Users need to know forecast confidence, not only the central estimate.
GenCast’s ensemble is designed for this use. Aurora 1.5 also offers probabilistic ensemble forecasting, but the two systems should not be treated as interchangeable without checking their output format, calibration, variables, and available checkpoints.
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| Model and source | Baseline | Reported result | Scope that qualifies the result |
|---|---|---|---|
| GraphCast, Google DeepMind (2023) | ECMWF deterministic HRES | Better on 89.3% of 2,760 target variables and lead times | Deterministic global medium-range evaluation; headline system reaches 10 days. |
| GenCast, Google DeepMind (2024) | ECMWF ENS | Better on 97.2% of 1,320 targets; 99.8% beyond 36 hours | Probabilistic ensemble comparison; 50 or more trajectories to 15 days. |
| Aurora 1.5, Microsoft Research (current project page accessed 2026) | ECMWF ensemble | Better on 88.9% of evaluated targets for days 1–10 | Different model version, target definition, and verification design from the other figures. |
These are percentages of evaluated targets, not percentages of forecasts that will be correct in a particular town tomorrow. “Target” can combine a variable with a lead time or another verification dimension. The baseline also matters: a deterministic model compared with HRES is not being tested under the same conditions as an ensemble model compared with ENS.
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For a serious comparison, record at least the model version, initialization and verification period, geography, variables, lead-time range, baseline, scoring rule, and whether the result measures deterministic accuracy, probabilistic skill, calibration, or another property. Without those details, a larger percentage can create a false impression of superiority.
Which model is best for common use cases?
Fast global maps and a single best estimate: GraphCast
Choose GraphCast when your product needs a coherent global forecast through 10 days and values simple, repeatable output. Its open reference implementation also makes it a practical starting point for researchers who need a commonly cited baseline. The reported sub-minute TPU v4 inference makes rapid experimentation possible when the required hardware and input pipeline are available.
GraphCast is not automatically the right choice when users need confidence bands, tail probabilities, or a horizon beyond 10 days. In those cases, forcing an ensemble question into a single deterministic field discards information.
Energy, logistics, agriculture, and other risk decisions: GenCast
GenCast is the strongest editorial recommendation when uncertainty is part of the decision. Renewable-energy operators can examine a range of wind or solar conditions; logistics teams can plan around multiple temperature or precipitation scenarios; emergency planners can estimate the chance of crossing a dangerous threshold.
The 15-day horizon is also meaningful for planning, but a longer horizon is not automatically more accurate. Ensemble spread should be examined alongside calibration and the decision’s tolerance for false alarms and missed events.
Multi-variable products and model adaptation: Aurora
Aurora is the best fit when the application needs a broad set of atmospheric outputs, hourly resolution, or a foundation model that can transfer to related tasks. Aurora 1.5’s 22 additional forecast variables and ensemble mode expand the range of products that can be built from one model family.
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Before selecting Aurora, verify which variables, checkpoints, inference code, and output resolutions are actually available for your intended deployment. A project page can describe capabilities without guaranteeing that every configuration is packaged for production use.
Strict scientific ranking: use a controlled test, not a headline
If your goal is to declare one model most accurate, none of the published headline percentages is sufficient by itself. Run all candidate systems on the same initial conditions, forecast dates, variables, grid, lead times, and scoring rules, or use a benchmark that explicitly harmonizes those choices. Otherwise, select by decision need rather than by cross-paper percentage.
Hurricanes, rapid intensification, and extreme weather
Advanced AI weather models can improve many forecast fields without eliminating the hardest failure modes. Microsoft notes that rapid intensification and peak wind speeds remain difficult, citing Storm Ciarán as an example. A model that performs strongly across broad target sets can still miss the timing or maximum intensity of a particular storm.
For hurricane or other high-impact applications, use ensembles and inspect the full range of plausible tracks and intensities where available. Do not treat a single AI trajectory as an official warning. Google’s GenCast description also emphasizes that conventional numerical models remain essential because they supply training data and initial conditions.
- Use the model output as additional guidance, not as a replacement for national warning services or operational meteorologists.
- Check whether the product verifies peak intensity, rapid change, landfall timing, and precipitation separately; an aggregate score may hide weakness in any one of them.
- Preserve ensemble spread and scenario information instead of displaying only the mean or one selected member.
- Escalate decisions when independent guidance disagrees or when the event is near a critical threshold.
Can you run these models yourself?
Running an AI weather model is more than downloading a neural-network checkpoint. You need atmospheric analysis data in the format the model expects, preprocessing and normalization, accelerator memory, inference code, and a way to write or visualize large multidimensional outputs.
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GraphCast requirements
GraphCast’s headline configuration uses two recent atmospheric states and forecasts on a 0.25-degree grid. Its widely cited open reference implementation makes it the clearest starting point for an independent experiment. You still need compatible analysis fields, a supported software environment, and substantial storage for global grids and forecast steps.
The published timing of less than a minute applies to one 10-day forecast on a Google TPU v4. If you lack that accelerator, plan for different latency and memory behavior; do not budget a deployment from the TPU figure alone.
GenCast requirements
GenCast produces at least 50 possible trajectories, so an experiment must handle ensemble storage, aggregation, and probabilistic verification rather than one forecast file. A 15-day ensemble can be generated in eight minutes on one Google Cloud TPU v5 according to Google’s description. The practical cost depends on the number of ensemble members, initialization dates, variables, precision, and hardware access.
GenCast also illustrates why initial conditions matter. Conventional numerical models remain part of the ecosystem because they provide training data and starting analyses. An AI model cannot recover information that is absent or badly represented in its input analysis.
Aurora requirements
Aurora deployments depend on the release associated with the specific Aurora or Aurora 1.5 configuration you intend to use. Confirm the available weights, inference software, variables, hourly or other output cadence, and ensemble support before committing to a pipeline. Foundation-model breadth does not remove the need for domain-specific validation.
A practical evaluation sequence
- Define the decision. Specify whether you need one field, probabilities, thresholds, extremes, or a 15-day planning horizon.
- Fix the verification design. Choose dates, geography, variables, lead times, grid, and scoring rules before looking at results.
- Prepare identical inputs. Use the same source analyses and carefully document any model-specific regridding or preprocessing.
- Run a small pilot. Test a few initialization dates to expose memory, latency, missing-variable, and output-format problems.
- Measure the right skill. Use deterministic scores for single forecasts and probabilistic scores, reliability, and sharpness checks for ensembles.
- Stress high-impact cases. Include storms, rapid changes, and threshold events rather than relying only on average conditions.
- Document uncertainty. Record model version, checkpoint, hardware, input source, ensemble size, and any post-processing.
What all three models cannot do
AI inference is fast, but speed does not make the atmosphere predictable without limit. Small errors in the initial state can grow, especially at longer lead times and during unstable weather. All three systems depend on high-quality atmospheric analyses and careful verification.
Broad benchmark performance also does not guarantee local accuracy. A global 0.25-degree grid cannot represent every street, valley, coastline, or convective cell. Downscaling and local observations may be necessary for an application whose decision threshold is highly local.
Finally, “better than ECMWF” is always shorthand for a specified experiment. It does not mean that an AI model replaces ECMWF’s operational infrastructure, data assimilation, warning expertise, or numerical-model ensemble. The strongest deployment pattern is often complementary: use AI for speed, additional scenarios, or specialized variables while retaining established meteorological operations for warnings and accountability.
A decision framework you can apply
| If your priority is… | Start with… | Validate… |
|---|---|---|
| Fastest path to one global forecast | GraphCast | Input availability, TPU or accelerator latency, and deterministic error by lead time. |
| Risk estimates and tail scenarios | GenCast | Calibration, ensemble spread, threshold probabilities, and storage cost for 50 or more members. |
| Hourly, multi-variable, transferable modeling | Aurora 1.5 | Which of the 22 additional variables and ensemble features are released for your target workflow. |
| Official warnings or life-safety decisions | A complementary operational workflow | Agreement with authoritative meteorological guidance, especially for rapid intensification and peak winds. |
| Publishing a universal leaderboard | None of the headline figures alone | A harmonized benchmark with identical data, targets, lead times, and scoring. |
Bottom line
Pick GenCast when uncertainty is the product, GraphCast when a fast deterministic 10-day reference is the product, and Aurora when breadth, hourly output, and foundation-model transfer are the product. The published percentages support those use-case distinctions, not a universal winner. For extreme weather, treat every model as decision support and keep ensemble information and operational meteorology in the loop.
Quick Recap
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