GraphCast

GlobalMRF202240 GBGoogleJAX

GraphCast is a graph neural network weather model from Google DeepMind that runs on a multi-mesh icosahedral representation of the globe. The version scored here is the operational configuration with 13 pressure levels at 0.25°. It consumes the two most recent analysis frames (t-6 h and t0) plus solar forcing. Each step advances 6 hours on the native ERA5 721 × 1440 grid.

Skill

Pick a metric and variable; hover for exact values at each lead time. Use the Region selector for continental splits, the Month selector for seasonal (DJF/MAM/JJA/SON) and per-month skill against the all-month curve, the Init hour selector for skill by initialization time, the View selector for the skill of every initial condition, and the Baseline selector to overlay persistence and climatology reference forecasts.

Evaluation

Deterministic · 48 initial conditions · 14-day horizon · 83 variables

Scores are latitude-weighted (cos φ) and aggregated over the initial conditions. Evaluation is done against ERA5 fetched from ARCO. Initial conditions rotate through the 00Z/06Z/12Z/18Z hours.

Type	deterministic
Initial conditions	48 (2025)
Initial condition source	ARCO
Verification (ground truth)	ARCO
Lead times	6 h to 14 days
Variables scored	83
Metrics	RMSE, MAE, Log spectral distance
Regions	global, tropics, extra tropics, northern hemisphere, southern hemisphere, arctic, antarctica, europe, north america, north atlantic, north pacific, east asia, australia new zealand, south america, africa, asia, oceania

Variables

Scored output variables (83)

Name	Description	Unit	Group
msl	Mean sea level pressure	Pa	Surface
t2m	Temperature at 2m	K	Surface
tp06	Total precipitation accumulated over past 6 hours	m	Surface
u10m	U-component (eastward, zonal) of wind at 10 m	m s⁻¹	Surface
v10m	V-component (northward, meridional) of wind at 10 m	m s⁻¹	Surface
z50	Geopotential at 50 hPa	m² s⁻²	Geopotential
z100	Geopotential at 100 hPa	m² s⁻²	Geopotential
z150	Geopotential at 150 hPa	m² s⁻²	Geopotential
z200	Geopotential at 200 hPa	m² s⁻²	Geopotential
z250	Geopotential at 250 hPa	m² s⁻²	Geopotential
z300	Geopotential at 300 hPa	m² s⁻²	Geopotential
z400	Geopotential at 400 hPa	m² s⁻²	Geopotential
z500	Geopotential at 500 hPa	m² s⁻²	Geopotential
z600	Geopotential at 600 hPa	m² s⁻²	Geopotential
z700	Geopotential at 700 hPa	m² s⁻²	Geopotential
z850	Geopotential at 850 hPa	m² s⁻²	Geopotential
z925	Geopotential at 925 hPa	m² s⁻²	Geopotential
z1000	Geopotential at 1000 hPa	m² s⁻²	Geopotential
t50	Temperature at 50 hPa	K	Temperature
t100	Temperature at 100 hPa	K	Temperature
t150	Temperature at 150 hPa	K	Temperature
t200	Temperature at 200 hPa	K	Temperature
t250	Temperature at 250 hPa	K	Temperature
t300	Temperature at 300 hPa	K	Temperature
t400	Temperature at 400 hPa	K	Temperature
t500	Temperature at 500 hPa	K	Temperature
t600	Temperature at 600 hPa	K	Temperature
t700	Temperature at 700 hPa	K	Temperature
t850	Temperature at 850 hPa	K	Temperature
t925	Temperature at 925 hPa	K	Temperature
t1000	Temperature at 1000 hPa	K	Temperature
u50	U-component of wind at 50 hPa	m s⁻¹	U wind
u100	U-component of wind at 100 hPa	m s⁻¹	U wind
u150	U-component of wind at 150 hPa	m s⁻¹	U wind
u200	U-component of wind at 200 hPa	m s⁻¹	U wind
u250	U-component of wind at 250 hPa	m s⁻¹	U wind
u300	U-component of wind at 300 hPa	m s⁻¹	U wind
u400	U-component of wind at 400 hPa	m s⁻¹	U wind
u500	U-component of wind at 500 hPa	m s⁻¹	U wind
u600	U-component of wind at 600 hPa	m s⁻¹	U wind
u700	U-component of wind at 700 hPa	m s⁻¹	U wind
u850	U-component of wind at 850 hPa	m s⁻¹	U wind
u925	U-component of wind at 925 hPa	m s⁻¹	U wind
u1000	U-component of wind at 1000 hPa	m s⁻¹	U wind
v50	V-component of wind at 50 hPa	m s⁻¹	V wind
v100	V-component of wind at 100 hPa	m s⁻¹	V wind
v150	V-component of wind at 150 hPa	m s⁻¹	V wind
v200	V-component of wind at 200 hPa	m s⁻¹	V wind
v250	V-component of wind at 250 hPa	m s⁻¹	V wind
v300	V-component of wind at 300 hPa	m s⁻¹	V wind
v400	V-component of wind at 400 hPa	m s⁻¹	V wind
v500	V-component of wind at 500 hPa	m s⁻¹	V wind
v600	V-component of wind at 600 hPa	m s⁻¹	V wind
v700	V-component of wind at 700 hPa	m s⁻¹	V wind
v850	V-component of wind at 850 hPa	m s⁻¹	V wind
v925	V-component of wind at 925 hPa	m s⁻¹	V wind
v1000	V-component of wind at 1000 hPa	m s⁻¹	V wind
q50	Specific humidity at 50 hPa	kg kg⁻¹	Specific humidity
q100	Specific humidity at 100 hPa	kg kg⁻¹	Specific humidity
q150	Specific humidity at 150 hPa	kg kg⁻¹	Specific humidity
q200	Specific humidity at 200 hPa	kg kg⁻¹	Specific humidity
q250	Specific humidity at 250 hPa	kg kg⁻¹	Specific humidity
q300	Specific humidity at 300 hPa	kg kg⁻¹	Specific humidity
q400	Specific humidity at 400 hPa	kg kg⁻¹	Specific humidity
q500	Specific humidity at 500 hPa	kg kg⁻¹	Specific humidity
q600	Specific humidity at 600 hPa	kg kg⁻¹	Specific humidity
q700	Specific humidity at 700 hPa	kg kg⁻¹	Specific humidity
q850	Specific humidity at 850 hPa	kg kg⁻¹	Specific humidity
q925	Specific humidity at 925 hPa	kg kg⁻¹	Specific humidity
q1000	Specific humidity at 1000 hPa	kg kg⁻¹	Specific humidity
w50	Vertical wind at 50 hPa	Pa s⁻¹	Vertical velocity
w100	Vertical wind at 100 hPa	Pa s⁻¹	Vertical velocity
w150	Vertical wind at 150 hPa	Pa s⁻¹	Vertical velocity
w200	Vertical wind at 200 hPa	Pa s⁻¹	Vertical velocity
w250	Vertical wind at 250 hPa	Pa s⁻¹	Vertical velocity
w300	Vertical wind at 300 hPa	Pa s⁻¹	Vertical velocity
w400	Vertical wind at 400 hPa	Pa s⁻¹	Vertical velocity
w500	Vertical wind at 500 hPa	Pa s⁻¹	Vertical velocity
w600	Vertical wind at 600 hPa	Pa s⁻¹	Vertical velocity
w700	Vertical wind at 700 hPa	Pa s⁻¹	Vertical velocity
w850	Vertical wind at 850 hPa	Pa s⁻¹	Vertical velocity
w925	Vertical wind at 925 hPa	Pa s⁻¹	Vertical velocity
w1000	Vertical wind at 1000 hPa	Pa s⁻¹	Vertical velocity

All of the model's output variables that have ERA5 verification are scored.

Data

The numbers behind the plot are in eval_scores_graphcast.json, exported by the eval recipe scorecard (scorecard/export_scores.py --docs) -- one value per metric, variable and lead time, in the variable's own units.

Reproducibility

Run and environment details

Date scored	2026-08-31
Scores written	2026-08-31
GPUs	8 x NVIDIA H100 80GB HBM3 (single node)
PyTorch	2.12.0a0+0291f960b6.nv26.04.48445190
CUDA	13.2
Python	3.12.3
Repo commit	a2cc76332120
Provenance source	run
Exported	2026-09-01
Locked dependencies	uv.lock @ a2cc76332120

Reference

Lam, R., et al. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), 1416-1421.