15 citations as recorded by crossref.

1. Projection of surface air temperature and precipitation in High Mountain Asia and its major upstream river basins through multi-model ensemble from CMIP6 S. Wang et al. https://doi.org/10.1007/s11629-025-9969-5
2. Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6 D. Lafferty & R. Sriver https://doi.org/10.1038/s41612-023-00486-0
3. Quantifying Impact of Geographical Variables on Climate Change Patterns over Spain by Time Series Clustering A. Palacios-Gutiérrez et al. https://doi.org/10.1007/s41748-025-00568-4
4. Inter-rater reliability adaptive weighting (IRRAWE) - a novel ensemble scheme for improved precipitation projections using CMIP6 climate models R. Khalil & Z. Ali https://doi.org/10.1007/s00704-025-05947-5
5. Projected changes in daily and multi-day extreme rainfall across the U.S. using downscaled CMIP6 models* G. Perez et al. https://doi.org/10.1088/2752-5295/ae4f14
6. Emergent constraints reveal underprediction of future global water availability under anthropogenic forcing S. Tang et al. https://doi.org/10.1016/j.gloplacha.2025.105252
7. Hosting downscaled decision-relevant community data products in ESGF2-US E. Massoud et al. https://doi.org/10.1088/2752-5295/ae27ea
8. Optimizing the multi-model ensemble of CMIP6 GCMs for climate simulation over Bangladesh A. Talukder et al. https://doi.org/10.1038/s41598-025-96446-0
9. Future meteorological drought in Ethiopia using better performed ensemble Coupled Model Intercomparison Project 6 (CIMP6) climate models Z. Kebede & G. Wedajo https://doi.org/10.1016/j.nhres.2025.08.006
10. Development of DRIP - drought representation index for CMIP climate model performance, application to Southeast Brazil L. Almeida et al. https://doi.org/10.1016/j.scitotenv.2024.176443
11. Future droughts in the Paraíba do Sul River Basin, Brazil: climate model selection based on performance and multi-model projections L. Almeida et al. https://doi.org/10.1590/2318-0331.312620250175
12. Evaluating statistical and machine learning bias-correction methods for CMIP6 rainfall projections in a tropical megacity N. Miniandi et al. https://doi.org/10.1007/s00704-026-06223-w
13. Bayesian weighting of climate models based on climate sensitivity E. Massoud et al. https://doi.org/10.1038/s43247-023-01009-8
14. Optimizing GCM ensemble selection and weighted MME development for improved drought projection under global climate models simulations M. Shakeel et al. https://doi.org/10.1007/s11069-026-08082-0
15. Future Changes in Hydro-Climatic Extremes across Vietnam: Evidence from a Semi-Distributed Hydrological Model Forced by Downscaled CMIP6 Climate Data H. Do et al. https://doi.org/10.3390/w16050674