Projected Aridity Dynamics Across the Western Balkans Using a Multi-Model CMIP6 Ensemble and Short-Term AI Benchmarking

The Western Balkans (Serbia, Croatia, Bosnia and Herzegovina, and Montenegro) occupy a transitional climatic position between the Mediterranean hotspot and the continental Balkan interior within Southeast Europe, yet multi-country, multi-model, station-resolved assessments of regional aridification remain scarce. We combined quality-controlled monthly temperature and precipitation records from 100 stations across Serbia, Croatia, Bosnia and Herzegovina, and Montenegro (1961–2020) with bias-corrected projections from a five-member CMIP6 ensemble (EC-Earth3, MPI-ESM1-2-HR, CNRM-CM6-1, MRI-ESM2-0, IPSL-CM6A-LR) under four SSP scenarios to 2100 and benchmarked these projections against five short-term forecasting baselines on a held-out 2019–2020 period. Aridity was quantified using the Ellenberg Climate Quotient (EQ) and De Martonne Index. Results: Ninety-eight of 100 stations showed significant warming (1961–2020, p < 0.05), and 26 showed significant aridification. Ensemble mean end-of-century EQ change ranged from −6.9% (SSP1-2.6) to +44.6% (SSP5-8.5, Montenegro), with the largest absolute increases in the Pannonian lowlands; inter-model uncertainty exceeded inter-scenario uncertainty by roughly a factor of two. Deep learning forecasters (TFT, N-HiTS) outperformed bias-corrected CMIP6 output for short-term, station-scale temperature forecasting, while a simple climatological baseline remained competitive for precipitation. CMIP6 projections and AI forecasting are complementary: multi-model ensembles remain indispensable for long-term, scenario-conditioned planning, while AI offers superior near-term predictive skill for operational decisions.

Authors

Institutions

Publication Details

Journal
Atmosphere
Published
2026-07-23
DOI
https://doi.org/10.3390/atmos17080712
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Projected Aridity Dynamics Across the Western Balkans Using a Multi-Model CMIP6 Ensemble and Short-Term AI Benchmarking

Mlađen Jovanović, Rastislav Stojsavljević, Ivica Djalović, Dalibor Nikolić et al.
Atmosphere
Climate variability and models
article

Projected Aridity Dynamics Across the Western Balkans Using a Multi-Model CMIP6 Ensemble and Short-Term AI Benchmarking

Mlađen Jovanović, Rastislav Stojsavljević, Ivica Djalović, Dalibor Nikolić, Dejan Stojanović
article en

Abstract

The Western Balkans (Serbia, Croatia, Bosnia and Herzegovina, and Montenegro) occupy a transitional climatic position between the Mediterranean hotspot and the continental Balkan interior within Southeast Europe, yet multi-country, multi-model, station-resolved assessments of regional aridification remain scarce. We combined quality-controlled monthly temperature and precipitation records from 100 stations across Serbia, Croatia, Bosnia and Herzegovina, and Montenegro (1961–2020) with bias-corrected projections from a five-member CMIP6 ensemble (EC-Earth3, MPI-ESM1-2-HR, CNRM-CM6-1, MRI-ESM2-0, IPSL-CM6A-LR) under four SSP scenarios to 2100 and benchmarked these projections against five short-term forecasting baselines on a held-out 2019–2020 period. Aridity was quantified using the Ellenberg Climate Quotient (EQ) and De Martonne Index. Results: Ninety-eight of 100 stations showed significant warming (1961–2020, p < 0.05), and 26 showed significant aridification. Ensemble mean end-of-century EQ change ranged from −6.9% (SSP1-2.6) to +44.6% (SSP5-8.5, Montenegro), with the largest absolute increases in the Pannonian lowlands; inter-model uncertainty exceeded inter-scenario uncertainty by roughly a factor of two. Deep learning forecasters (TFT, N-HiTS) outperformed bias-corrected CMIP6 output for short-term, station-scale temperature forecasting, while a simple climatological baseline remained competitive for precipitation. CMIP6 projections and AI forecasting are complementary: multi-model ensembles remain indispensable for long-term, scenario-conditioned planning, while AI offers superior near-term predictive skill for operational decisions.

AtmosphereVol. 17(8)
University of Kragujevac (RS), University of Novi Sad (RS), Institute of Field and Vegetable Crops (RS), Institute of Lowland Forestry and Environment (RS)
Provincial Secretariat for Science and Technological Development
Climate action
Openalex Percentile: Top 11%
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.