94 citations as recorded by crossref.

1. Effects of anthropogenic climate change on the compound drought and heat events in different agricultural regions of China J. Yang et al. 10.1016/j.ecolind.2024.112719
2. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
3. A non-stationary extreme-value approach for climate projection ensembles: application to snow loads in the French Alps E. Le Roux et al. 10.5194/esd-13-1059-2022
4. To what extent horizontal resolution improves the simulation of precipitation in CMIP6 HighResMIP models over Southwest China? Z. Jin et al. 10.3389/feart.2022.1003748
5. Selecting and weighting dynamical models using data-driven approaches P. Le Bras et al. 10.5194/npg-31-303-2024
6. Application-specific optimal model weighting of global climate models: A red tide example A. Elshall et al. 10.1016/j.cliser.2022.100334
7. Impacts of Climate Change on Extreme Precipitation Events and Urban Waterlogging: A Case Study of Beijing X. Ji et al. 10.1061/NHREFO.NHENG-1889
8. Combining global climate models using graph cuts S. Thao et al. 10.1007/s00382-022-06213-4
9. Projected increase in droughts over the Arabian Peninsula and associated uncertainties M. Saharwardi et al. 10.1038/s41598-025-85863-w
10. Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land L. Beusch et al. 10.5194/esd-11-139-2020
11. Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models A. Snyder et al. 10.5194/esd-15-1301-2024
12. Multimodel Errors and Emergence Times in Climate Attribution Studies P. Naveau & S. Thao 10.1175/JCLI-D-21-0332.1
13. Communicating potentially large but non‐robust changes in multi‐model projections of future climate G. Zappa et al. 10.1002/joc.7041
14. Common Error Patterns in the Regional Atmospheric Circulation Simulated by the CMIP Multi‐Model Ensemble S. Brands 10.1029/2022GL101446
15. Evaluation of methods for selecting climate models to simulate future hydrological change A. Ross & R. Najjar 10.1007/s10584-019-02512-8
16. Wetter California Projected by CMIP6 Models With Observational Constraints Under a High GHG Emission Scenario F. Li et al. 10.1029/2022EF002694
17. Mutual information based weighted variance approach for uncertainty quantification of climate projections A. Majhi et al. 10.1016/j.mex.2023.102063
18. The hydrological cycle and ocean circulation of the Maritime Continent in the Pliocene: results from PlioMIP2 X. Ren et al. 10.5194/cp-19-2053-2023
19. Probabilistic projections of El Niño Southern Oscillation properties accounting for model dependence and skill R. Olson et al. 10.1038/s41598-022-26513-3
20. On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results G. Abramowitz et al. 10.5194/bg-21-5517-2024
21. Comparing Methods to Constrain Future European Climate Projections Using a Consistent Framework L. Brunner et al. 10.1175/JCLI-D-19-0953.1
22. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project S. Hantson et al. 10.5194/gmd-13-3299-2020
23. Temperature and Precipitation Extremes over the Iberian Peninsula under Climate Change Scenarios: A Review S. Pereira et al. 10.3390/cli9090139
24. A CMIP6-based multi-model downscaling ensemble to underpin climate change services in Australia M. Grose et al. 10.1016/j.cliser.2023.100368
25. Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria I. Hassan et al. 10.3390/w12020385
26. Addressing partial identification in climate modeling and policy analysis C. Manski et al. 10.1073/pnas.2022886118
27. Potential for historically unprecedented Australian droughts from natural variability and climate change G. Falster et al. 10.5194/hess-28-1383-2024
28. Sustainability of Arctic Ecosystems in a Changing Climate O. Anisimov & E. Zhil’tsova 10.3103/S1068373922050077
29. How do value-judgements enter model-based assessments of climate sensitivity? S. Undorf et al. 10.1007/s10584-022-03435-7
30. Are general circulation models obsolete? V. Balaji et al. 10.1073/pnas.2202075119
31. Physically based equation representing the forcing-driven precipitation in climate models D. Lee et al. 10.1088/1748-9326/acf50f
32. Predicted and Projected Water Resources Changes in the Chari Catchment, the Lake Chad Basin, Africa R. Mahmood et al. 10.1175/JHM-D-19-0105.1
33. Changes in mean and extreme temperature and precipitation events from different weighted multi-model ensembles over the northern half of Morocco S. Balhane et al. 10.1007/s00382-021-05910-w
34. A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting E. Bach & M. Ghil 10.1029/2022MS003123
35. RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections C. Jones et al. 10.1029/2023AV001024
36. The blessing of dimensionality for the analysis of climate data B. Christiansen 10.5194/npg-28-409-2021
37. Quantification of model uncertainty in sub-daily extreme precipitation projections A. Majhi et al. 10.1016/j.gloplacha.2022.103967
38. A dependent multimodel approach to climate prediction with Gaussian processes M. Thompson et al. 10.1017/eds.2022.24
39. Perturbed Biology and Physics Signatures in a 1-D Ocean Biogeochemical Model Ensemble P. Anugerahanti et al. 10.3389/fmars.2020.00549
40. Going Beyond the Ensemble Mean: Assessment of Future Floods From Global Multi‐Models I. Giuntoli et al. 10.1029/2020WR027897
41. Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications A. Merrifield et al. 10.5194/gmd-16-4715-2023
42. Explained predictions of strong eastern Pacific El Niño events using deep learning G. Rivera Tello et al. 10.1038/s41598-023-45739-3
43. Formally combining different lines of evidence in extreme-event attribution F. Otto et al. 10.5194/ascmo-10-159-2024
44. Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence M. Amos et al. 10.5194/acp-20-9961-2020
45. Reduced global warming from CMIP6 projections when weighting models by performance and independence L. Brunner et al. 10.5194/esd-11-995-2020
46. Exposure of mammal genetic diversity to mid‐21st century global change S. Theodoridis et al. 10.1111/ecog.05588
47. Evolution of land surface feedbacks on extreme heat: Adapting existing coupling metrics to a changing climate P. Dirmeyer et al. 10.3389/fenvs.2022.949250
48. Combination of Decadal Predictions and Climate Projections in Time: Challenges and Potential Solutions D. Befort et al. 10.1029/2022GL098568
49. Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6 F. Lehner et al. 10.5194/esd-11-491-2020
50. Simulated Tropical Precipitation Assessed across Three Major Phases of the Coupled Model Intercomparison Project (CMIP) S. Fiedler et al. 10.1175/MWR-D-19-0404.1
51. A Markov chain method for weighting climate model ensembles M. Kulinich et al. 10.5194/gmd-14-3539-2021
52. Spatial partitioning of terrestrial precipitation reveals varying dataset agreement across different environments Y. Markonis et al. 10.1038/s43247-024-01377-9
53. Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity P. Kuma et al. 10.1029/2022MS003588
54. The optimization of model ensemble composition and size can enhance the robustness of crop yield projections L. Li et al. 10.1038/s43247-023-01016-9
55. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. 10.5194/hess-25-3105-2021
56. From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.3) L. Beusch et al. 10.5194/gmd-15-2085-2022
57. Why we need lower-performance climate models R. O’Loughlin 10.1007/s10584-023-03661-7
58. Effects of multi-observations uncertainty and models similarity on climate change projections R. Pathak et al. 10.1038/s41612-023-00473-5
59. Polar ice sheets are decisive contributors to uncertainty in climate tipping projections J. Rosser et al. 10.1038/s43247-024-01799-5
60. Dynamical downscaling CMIP6 models over New Zealand: added value of climatology and extremes P. Gibson et al. 10.1007/s00382-024-07337-5
61. Investigating the ability of CMIP6 HighResMIP models to simulate rainy season precipitation over the Lancang-Mekong River Basin D. Yao et al. 10.20517/dpr.2024.02
62. Assessing uncertainty for decision‐making in climate adaptation and risk mitigation P. Reggiani et al. 10.1002/joc.6996
63. Assessing the Impacts of Land Use, Land Cover, and Climate Change on the Hydrological Regime of a Humid Tropical Basin A. Abraham & S. Kundapura 10.1061/NHREFO.NHENG-1801
64. Time Variability Correction of CMIP6 Climate Change Projections Y. Shao et al. 10.1029/2023MS003640
65. Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle L. Teckentrup et al. 10.5194/esd-14-549-2023
66. Multimodel ensemble analysis with neural network Gaussian processes T. Harris et al. 10.1214/23-AOAS1768
67. Showcasing MESMER‐X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models Y. Quilcaille et al. 10.1029/2022GL099012
68. Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6 E. Holtanová et al. 10.3389/feart.2022.1018661
69. An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles A. Merrifield et al. 10.5194/esd-11-807-2020
70. Robust historical evapotranspiration trends across climate regimes S. Hobeichi et al. 10.5194/hess-25-3855-2021
71. Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems V. Srikrishnan et al. 10.1029/2021EF002644
72. Understanding the Distribution of Multimodel Ensembles B. Christiansen 10.1175/JCLI-D-20-0186.1
73. Does the weighting of climate simulations result in a better quantification of hydrological impacts? H. Wang et al. 10.5194/hess-23-4033-2019
74. Crossbreeding CMIP6 Earth System Models With an Emulator for Regionally Optimized Land Temperature Projections L. Beusch et al. 10.1029/2019GL086812
75. An Approach for Selecting Observationally-Constrained Global Climate Model Ensembles for Regional Climate Impacts and Adaptation Studies in Canada D. Jeong & A. Cannon 10.1080/07055900.2023.2239194
76. Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America M. Reboita et al. 10.1088/2752-5295/ad3fdb
77. Distribution-based pooling for combination and multi-model bias correction of climate simulations M. Vrac et al. 10.5194/esd-15-735-2024
78. More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century N. Maher et al. 10.1038/s41467-020-20635-w
79. Plant profit maximization improves predictions of European forest responses to drought M. Sabot et al. 10.1111/nph.16376
80. Assessment of Precipitation and Hydrological Droughts in South America through Statistically Downscaled CMIP6 Projections G. Ferreira et al. 10.3390/cli11080166
81. Air–Sea CO2 Exchange Over the Mediterranean Sea, the Red Sea and the Arabian Sea M. Zarghamipour et al. 10.1007/s41742-024-00586-6
82. Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions M. Castaneda-Gonzalez et al. 10.1007/s10584-023-03643-9
83. Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. 10.5194/hess-26-1481-2022
84. Towards Winter Seasonal Predictability of the North West European Shelf Seas J. Tinker & L. Hermanson 10.3389/fmars.2021.698997
85. Future precipitation changes in California: Comparison of CMIP5 and CMIP6 intermodel spread and its drivers D. Petrova et al. 10.1002/joc.8449
86. Robustness reasoning in climate model comparisons R. O’Loughlin 10.1016/j.shpsa.2020.12.005
87. Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification H. Chen et al. 10.5194/gmd-16-5685-2023
88. On the relation of CMIP6 GCMs errors at RCM driving boundary condition zones and inner region for Central Europe region E. Holtanová et al. 10.1007/s00382-024-07216-z
89. Quantifying uncertainty in European climate projections using combined performance-independence weighting L. Brunner et al. 10.1088/1748-9326/ab492f
90. Developing Representative Impact Scenarios From Climate Projection Ensembles, With Application to UKCP18 and EURO‐CORDEX Precipitation S. Jewson et al. 10.1029/2022MS003038
91. Modeling and emulation of nonstationary Gaussian fields D. Nychka et al. 10.1016/j.spasta.2018.08.006
92. Model spread and progress in climate modelling J. Jebeile & A. Barberousse 10.1007/s13194-021-00387-0
93. Taking climate model evaluation to the next level V. Eyring et al. 10.1038/s41558-018-0355-y
94. Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling A. Schäfer Rodrigues Silva et al. 10.1007/s40808-022-01427-1