Pith Number

pith:ORNWSTWF

pith:2026:ORNWSTWFN2E54ABJSCQQW4KZWS

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Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior

Daniele Visioni, Yue Wang

Longwang enables zero-shot downscaling of global precipitation to daily 10 km fields from monthly 100 km inputs by combining a context-conditioned latent generative prior with posterior sampling.

arxiv:2605.17603 v1 · 2026-05-17 · physics.ao-ph · cs.LG

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Claims

C1strongest claim

On ERA5 reanalysis, Longwang outperforms standard posterior sampling with an unconditional generative prior in reconstructing fine-scale spatial patterns, preserving temporal coherence, and recovering extreme precipitation intensities. The framework further generalizes to historical climate simulations and future climate projections under substantial distribution shift.

C2weakest assumption

The assumption that a context-conditioned latent generative prior learned in an unsupervised or self-supervised manner can be effectively combined with a physically informed observation operator to produce accurate posterior samples that generalize across significant distribution shifts in climate data.

C3one line summary

Longwang enables zero-shot downscaling of global precipitation to daily 10 km resolution from monthly 100 km data by learning a context-conditioned latent generative prior and using posterior sampling with a physical observation operator.

References

47 extracted · 47 resolved · 3 Pith anchors

[1] Zhang, W., Zhou, T. & Wu, P. Anthropogenic amplification of precipitation variability over the past century.Science385, 427–432 (2024). 20 2024

[2] Schneider, T.et al.Climate goals and computing the future of clouds.Nature Climate Change7, 3–5 (2017) 2017

[3] & Gutowski Jr, W 2015

[4] Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production.Nature601, 223–227 (2022) 2022

[5] Hess, P. & Boers, N. Deep learning for improving numerical weather predic- tion of heavy rainfall.Journal of Advances in Modeling Earth Systems14, e2021MS002765 (2022) 2022

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Receipt and verification

First computed	2026-05-20T00:04:48.063917Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

745b694ec56e89de002990a10b7159b487770f1252e7da7aa157126d32d8de18

Aliases

arxiv: 2605.17603 · arxiv_version: 2605.17603v1 · doi: 10.48550/arxiv.2605.17603 · pith_short_12: ORNWSTWFN2E5 · pith_short_16: ORNWSTWFN2E54ABJ · pith_short_8: ORNWSTWF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ORNWSTWFN2E54ABJSCQQW4KZWS \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 745b694ec56e89de002990a10b7159b487770f1252e7da7aa157126d32d8de18

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "a3c687ae518b446c0bbd84ca89beaaf5ccddd63d2cd9dcf80e17163c75529bbb",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.ao-ph",
    "submitted_at": "2026-05-17T19:01:47Z",
    "title_canon_sha256": "cf1ed957db554944efbac8381d44e89ac73ac55b19fb4da81d594931b314cc1d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17603",
    "kind": "arxiv",
    "version": 1
  }
}