Multi-annual predictions of hot, dry and hot-dry compound extremes

Abstract. Hot-dry compound extremes have recently gained increasing attention due to their potential impacts on environments and societies. For these reasons, assessing climate predictions is essential to providing reliable information on such extremes. However, despite several studies focusing on compound extremes in the past and climate projections, little is known on a multi-annual timescale. In this regard, decadal climate predictions have been produced to provide useful information for this specific timescale. Thus, we evaluate the ability of the CMIP6 multi-model decadal climate hindcast to predict hot-dry climate extremes, as well as their hot and dry univariate counterparts, for the forecast years 2–5. The multi-model skillfully predicts hot-dry compound extremes and hot extremes over most land regions, while the skill is more limited for dry extremes. However, we find only minor and spatially limited improvements from the initialisation of the hindcasts, especially for the hot-dry compound extremes, with most of the skill coming from external forcings, especially long-term trends. Finally, we find that the decadal hindcast is able to reproduce the connections between the compound extremes and their hot and dry univariate components. Evaluations of decadal hindcasts, such as this, are an essential tool for establishing the potential and limitations of these products. In turn, they represent a necessary step in providing reliable and valuable information regarding such impactful extremes.

Authors

Institutions

Publication Details

Journal
Earth System Dynamics
Published
2025-12-17
DOI
https://doi.org/10.5194/esd-16-2225-2025
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-annual predictions of hot, dry and hot-dry compound extremes

Paolo De Luca, Markus G. Donat, Margarida Samsó, Alvise Aranyossy et al.
Earth System Dynamics
Climate variability and models
article

Multi-annual predictions of hot, dry and hot-dry compound extremes

Paolo De Luca, Markus G. Donat, Margarida Samsó, Alvise Aranyossy, Carlos Delgado‐Torres, Balakrishnan Solaraju-Murali
article en

Abstract

Abstract. Hot-dry compound extremes have recently gained increasing attention due to their potential impacts on environments and societies. For these reasons, assessing climate predictions is essential to providing reliable information on such extremes. However, despite several studies focusing on compound extremes in the past and climate projections, little is known on a multi-annual timescale. In this regard, decadal climate predictions have been produced to provide useful information for this specific timescale. Thus, we evaluate the ability of the CMIP6 multi-model decadal climate hindcast to predict hot-dry climate extremes, as well as their hot and dry univariate counterparts, for the forecast years 2–5. The multi-model skillfully predicts hot-dry compound extremes and hot extremes over most land regions, while the skill is more limited for dry extremes. However, we find only minor and spatially limited improvements from the initialisation of the hindcasts, especially for the hot-dry compound extremes, with most of the skill coming from external forcings, especially long-term trends. Finally, we find that the decadal hindcast is able to reproduce the connections between the compound extremes and their hot and dry univariate components. Evaluations of decadal hindcasts, such as this, are an essential tool for establishing the potential and limitations of these products. In turn, they represent a necessary step in providing reliable and valuable information regarding such impactful extremes.

Earth System DynamicsVol. 16(6)
Institució Catalana de Recerca i Estudis Avançats (ES), Zurich Insurance Group (Switzerland) (CH), Barcelona Supercomputing Center (ES), Universitat de Barcelona (ES), Universitat Politècnica de Catalunya (ES)
Openalex Percentile: Top 62%
Climate variability and models
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.