HeatCast-W34 Distributional Mesh GNN

HeatCast is a 4,637,891-parameter GraphCast-style mesh graph neural

network for probabilistic warm-season CONUS week-3--4 maximum-temperature

prediction.

Outputs

For every PRISM-grid land cell and lead from day 15 through day 28, HeatCast

produces a Gaussian conditional mean and state-dependent standard deviation.

It does not produce dynamically independent ensemble members.

Each folds/foldN/ directory contains the selected best-monitor model weights

in Safetensors format and the checkpoint architecture metadata. Five

year-disjoint folds are provided.

Research on retrospective CONUS MJJAS week-3--4 temperature prediction and

heat-exceedance calibration.

These are research hindcast models, not an operational warning system.
Training and evaluation target PRISM daily maximum temperature over CONUS.
Exact inference requires the HeatCast preprocessing inputs and normalization

conventions described in MODEL_INPUTS.md.
PRISM, ERA5, ECMWF ENS, and other external datasets are not distributed here.
HeatCast weights provide mean and sigma. HeatCast-C probability calibration

and the HeatCast-plus-ENS stack require additional fold-safe calibration and

ECMWF ENS artifacts.
Performance outside CONUS, May through September, or the historical record

has not been established.

The model uses grid-to-mesh encoding, an icosahedral multimesh message-passing

processor, multi-lead tube attention, mesh-to-grid decoding, and a

distributional head trained using Gaussian CRPS.

MIT for the distributed HeatCast code and weights. External datasets retain

their original terms and licenses.

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