Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6663-2026
https://doi.org/10.5194/gmd-19-6663-2026
Model evaluation paper
 | 
23 Jul 2026
Model evaluation paper |  | 23 Jul 2026

Ensemble forecasts of isolated and compound wind and precipitation extremes in Europe using HC-SWG (v3.1) and MA-SWG (v1.1) Stochastic Weather Generators

Meriem Krouma and Gabriele Messori

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-3662', Anonymous Referee #1, 05 Jan 2026
  • RC2: 'Comment on egusphere-2025-3662', Anonymous Referee #2, 05 Jan 2026

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Meriem Krouma on behalf of the Authors (10 Feb 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Feb 2026) by Emmanouil Flaounas
RR by Anonymous Referee #2 (03 Mar 2026)
RR by Anonymous Referee #1 (19 Mar 2026)
ED: Reconsider after major revisions (27 Mar 2026) by Emmanouil Flaounas
AR by Meriem Krouma on behalf of the Authors (16 Apr 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (20 Apr 2026) by Emmanouil Flaounas
RR by Anonymous Referee #2 (22 Apr 2026)
ED: Publish as is (24 Apr 2026) by Emmanouil Flaounas
AR by Meriem Krouma on behalf of the Authors (25 Apr 2026)  Manuscript 

Post-review adjustments

AA – Author's adjustment | EA – Editor approval
AA by Meriem Krouma on behalf of the Authors (13 Jul 2026)   Author's adjustment   Manuscript
EA: Adjustments approved (17 Jul 2026) by Emmanouil Flaounas
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Short summary
We present two forecasting methods for extreme precipitation and extreme wind in Europe, using stochastic weather generators and past atmospheric patterns. One targets precipitation via weather model reforecasts; the other predicts wind from large-scale patterns. Both outperform standard weather models up to 10 d ahead, offering improved accuracy for both individual and compound extreme events.
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