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pith:NEBDNYI7

pith:2026:NEBDNYI7SUCWMT64C77G2ZSFAA

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Multi-Quantile Regression for Extreme Precipitation Downscaling

Gareth Lagerwall, Hamed Najafi, Jason Liu, Jayantha Obeysekera

A multi-quantile super-resolution network detects extreme precipitation up to 18 times better than deterministic baselines by using pinball loss on separate quantile heads.

arxiv:2605.12762 v1 · 2026-05-12 · cs.LG · cs.AI

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Claims

C1strongest claim

Under this design, data augmentation via cVAE becomes complementary: the median head absorbs synthetic patterns without contaminating upper quantiles. Empirically, on Florida (convective/tropical-cyclone dominated), the un-augmented Q-SRDRN P999 head detects 1,598 of 2,111 events at 200 mm/day versus 88 for the deterministic baseline--an 18x detection-rate gain (4.2% to 75.7%)--with 63% lower KL divergence and 3.9% lower RMSE.

C2weakest assumption

The primary obstacle is the loss function, not the data: under intensity-weighted MAE, real and synthetic labels at the same input are simply averaged, meaning data augmentation shifts the predicted mean rather than the conditional distribution.

C3one line summary

Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.

References

30 extracted · 30 resolved · 3 Pith anchors

[1] J. Ba\ no-Medina, R. Manzanas, and J. M. Guti \'e rrez. Configuration and intercomparison of deep learning neural models for statistical downscaling. Geoscientific Model Development, 13(4):2109--2124, 2020 · doi:10.5194/gmd-13-2109-2020

[2] Accurate medium-range global weather forecasting with 3d neural networks 2023 · doi:10.1038/s41586-023-06185-3

[3] A. Brando, J. Gimeno, J. A. Rodr\'iguez-Serrano, and J. Vitri\`a. Deep non-crossing quantiles through the partial derivative. In Proceedings of the 25th International Conference on Artificial Intellig 2022

[4] J. B. Bremnes. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Monthly Weather Review, 132(1):338--347, 2004. DOI: 10.1175/1520-0493(2004)132<0338:PFOPIT>2.0.CO; 2004 · doi:10.1175/1520-0493(2004)132

[5] A. J. Cannon. Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9):1277--1284, 2011. DOI: 10.1016/j.cageo.2010.07.005 h 2011 · doi:10.1016/j.cageo.2010.07.005

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Receipt and verification

First computed	2026-05-18T03:09:48.372310Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898

Aliases

arxiv: 2605.12762 · arxiv_version: 2605.12762v1 · doi: 10.48550/arxiv.2605.12762 · pith_short_12: NEBDNYI7SUCW · pith_short_16: NEBDNYI7SUCWMT64 · pith_short_8: NEBDNYI7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NEBDNYI7SUCWMT64C77G2ZSFAA \
  | 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: 690236e11f9505664fdc17fe6d66450032a617f0e5b5584791b3411e4df99898

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "50e23d3ed7a4fbe2512f885c4e0c789dd4bbc75f03380b6a3f92cbbaf28bcaaa",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-12T21:17:26Z",
    "title_canon_sha256": "97a5f8af62bdfc03879f9b8df485d51738456c8ee8c31a789ebc49e2fdcae8ff"
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}