118 citations as recorded by crossref.

1. Philosophical Perspectives on Earth System Modeling: Truth, Adequacy, and Understanding G. Gramelsberger et al. https://doi.org/10.1029/2019MS001720
2. Climate data uncertainty for agricultural impact assessments in West Africa P. Romanovska et al. https://doi.org/10.1007/s00704-023-04430-3
3. Air–Sea CO2 Exchange Over the Mediterranean Sea, the Red Sea and the Arabian Sea M. Zarghamipour et al. https://doi.org/10.1007/s41742-024-00586-6
4. Projections of summer light rain frequency in typical terrain over eastern China under wind speed constraint X. Dong et al. https://doi.org/10.1007/s11430-023-1331-6
5. Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States P. Gibson et al. https://doi.org/10.1175/JHM-D-18-0230.1
6. Optimizing category-based statistical metrics for selecting global climate models in rainfall projections for Peninsular Malaysia Z. Sa’adi et al. https://doi.org/10.1007/s00704-025-05952-8
7. Selecting CMIP6 GCMs for CORDEX Dynamical Downscaling: Model Performance, Independence, and Climate Change Signals G. Di Virgilio et al. https://doi.org/10.1029/2021EF002625
8. Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity S. Burrows et al. https://doi.org/10.1007/s00376-018-7300-x
9. Future surface mass balance and surface melt in the Amundsen sector of the West Antarctic Ice Sheet M. Donat-Magnin et al. https://doi.org/10.5194/tc-15-571-2021
10. Does the weighting of climate simulations result in a better quantification of hydrological impacts? H. Wang et al. https://doi.org/10.5194/hess-23-4033-2019
11. Towards a balancing performance, uncertainty coverage, and spatial consistency in climate model sub-selection I. Lagos-Castro et al. https://doi.org/10.1007/s10584-026-04161-0
12. Testing the assumptions in emergent constraints: why does the “emergent constraint on equilibrium climate sensitivity from global temperature variability” work for CMIP5 and not CMIP6? M. Williamson et al. https://doi.org/10.5194/esd-15-829-2024
13. Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations A. Dosio et al. https://doi.org/10.1007/s10584-022-03307-0
14. Towards a Computational Workflow for Studying the Effects of Climate Change on Wind Loads on High-Rise Buildings in Urban Areas A. Teran et al. https://doi.org/10.1080/07055900.2022.2061412
15. Intensification of extreme precipitation in arid Central Asia J. Yao et al. https://doi.org/10.1016/j.jhydrol.2020.125760
16. Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate N. Herger et al. https://doi.org/10.1029/2018JD028549
17. The effect of univariate bias adjustment on multivariate hazard estimates J. Zscheischler et al. https://doi.org/10.5194/esd-10-31-2019
18. Scenario Forecasts of the Climate Change in Belarus Based on Regionally Adapted Ensembles of CMIP6 Climate Models S. Lysenko https://doi.org/10.3103/S1068373925070052
19. Environmental assessment of climate-driven suitability and spatial shifts of agro-voltaic systems in india using ensemble modeling M. Mathur & S. Poonia https://doi.org/10.1007/s00704-026-06656-3
20. Intensification of precipitation extremes in the world’s humid and water-limited regions M. Donat et al. https://doi.org/10.1088/1748-9326/ab1c8e
21. CMIP6 model ranking and machine learning–assisted ensemble evaluation using a multi-variable integrated MCDM–GDM framework M. Narahari et al. https://doi.org/10.2166/wcc.2026.131
22. Developing Representative Impact Scenarios From Climate Projection Ensembles, With Application to UKCP18 and EURO‐CORDEX Precipitation S. Jewson et al. https://doi.org/10.1029/2022MS003038
23. Emergence of climate change signal in CMIP6 extreme indices N. Schuhen et al. https://doi.org/10.5194/nhess-26-753-2026
24. Using CNN framework to improve multi-GCM ensemble predictions of monthly precipitation at local areas: An application over China and comparison with other methods L. Sun et al. https://doi.org/10.1016/j.jhydrol.2023.129866
25. The blessing of dimensionality for the analysis of climate data B. Christiansen https://doi.org/10.5194/npg-28-409-2021
26. Evolution of drought and flood events on the Qinghai-Tibet Plateau and key issues for response J. Lu et al. https://doi.org/10.1007/s11430-022-1168-0
27. Seasonal hydrologic responses to climate change based on a CMIP6 multi-model ensemble: A case study of An-Seong watershed in South Korea S. Woo et al. https://doi.org/10.1016/j.ejrh.2025.102491
28. Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America R. Lorenz et al. https://doi.org/10.1029/2017JD027992
29. Latest climate models confirm need for urgent mitigation P. Forster et al. https://doi.org/10.1038/s41558-019-0660-0
30. Conserving Land–Atmosphere Synthesis Suite (CLASS) S. Hobeichi et al. https://doi.org/10.1175/JCLI-D-19-0036.1
31. Evaluation for Optimal Model Selection and Bias-Corrected Projections of Daytime and Nighttime Extreme Heat in China Using the SimmEX Dataset S. Li et al. https://doi.org/10.3390/su181910078
32. Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States E. Massoud et al. https://doi.org/10.1175/JHM-D-19-0258.1
33. Predictability of water resources with global climate models. Case of Northern Tunisia M. Besbes & J. Chahed https://doi.org/10.5802/crgeos.219
34. Projected changes in surface air temperature over Pakistan under bias-constrained CMIP6 models R. Karim et al. https://doi.org/10.1007/s12517-023-11243-1
35. Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective L. Qiu et al. https://doi.org/10.1088/1748-9326/abc8bb
36. Reduced global warming from CMIP6 projections when weighting models by performance and independence L. Brunner et al. https://doi.org/10.5194/esd-11-995-2020
37. Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications A. Merrifield et al. https://doi.org/10.5194/gmd-16-4715-2023
38. Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence J. Zhao et al. https://doi.org/10.1007/s00376-023-2393-2
39. A Markov chain method for weighting climate model ensembles M. Kulinich et al. https://doi.org/10.5194/gmd-14-3539-2021
40. Application-specific optimal model weighting of global climate models: A red tide example A. Elshall et al. https://doi.org/10.1016/j.cliser.2022.100334
41. Hydro‐Climatic Modelling for Water Resources: From Processes to Adaptive Management and Governance J. Chahed https://doi.org/10.1002/hyp.70348
42. Selecting and weighting dynamical models using data-driven approaches P. Le Bras et al. https://doi.org/10.5194/npg-31-303-2024
43. Comparing Methods to Constrain Future European Climate Projections Using a Consistent Framework L. Brunner et al. https://doi.org/10.1175/JCLI-D-19-0953.1
44. A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies F. Yakubu et al. https://doi.org/10.1038/s41597-026-07709-y
45. Multiple Metrics Informed Projections of Future Precipitation in China L. Wang et al. https://doi.org/10.1029/2021GL093810
46. Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains M. Zhang et al. https://doi.org/10.1007/s00382-022-06355-5
47. Projecting meteorological drought in Northern Malawi using SPEI and bias-corrected CMIP6 models I. Tchuwa & J. Patel https://doi.org/10.1007/s44292-025-00059-1
48. Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia D. Faidah et al. https://doi.org/10.1155/2022/9779829
49. Increased Extreme Precipitation in Western North America from Cut-Off Lows Under a Warming Climate H. Pinheiro et al. https://doi.org/10.3390/meteorology4020011
50. Ice-Free Conditions in the Arctic Projected by Score-Based Selected Models and Models’ Spread Analysis E. Shalina et al. https://doi.org/10.1007/s41748-025-00856-z
51. Simulation of factors affecting Emiliania huxleyi blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection N. Gnatiuk et al. https://doi.org/10.5194/bg-17-1199-2020
52. Extreme Heat Events over Southeast Europe Based on NEX-GDDP Ensemble: Present Climate Evaluation and Future Projections H. Chervenkov & K. Malcheva https://doi.org/10.3390/atmos14061000
53. Projection of Future Heat Waves in the United States. Part I: Selecting a Climate Model Subset J. Shafiei Shiva & D. Chandler https://doi.org/10.3390/atmos11060587
54. Underestimating the Challenges of Avoiding a Ghastly Future C. Bradshaw et al. https://doi.org/10.3389/fcosc.2020.615419
55. ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing G. Abramowitz et al. https://doi.org/10.5194/esd-10-91-2019
56. More accurate quantification of model-to-model agreement in externally forced climatic responses over the coming century N. Maher et al. https://doi.org/10.1038/s41467-020-20635-w
57. CMIP6 MultiModel Evaluation of Present‐Day Heatwave Attributes A. Hirsch et al. https://doi.org/10.1029/2021GL095161
58. Storylines of South Pacific Convergence Zone Changes in a Warmer World S. Narsey et al. https://doi.org/10.1175/JCLI-D-21-0433.1
59. Intercomparison of multi-model ensemble-processing strategies within a consistent framework for climate projection in China H. Zhu et al. https://doi.org/10.1007/s11430-022-1154-7
60. Which Climate Model Evaluation Methods Can Consistently Select Skillful Models from the CMIP6 Ensemble? N. Gnatiuk et al. https://doi.org/10.24057/2071-9388-2025-3694
61. A CMIP6-based multi-model downscaling ensemble to underpin climate change services in Australia M. Grose et al. https://doi.org/10.1016/j.cliser.2023.100368
62. Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle L. Teckentrup et al. https://doi.org/10.5194/esd-14-549-2023
63. Ranking CMIP5 GCMs for Model Ensemble Selection on Regional Scale: Case Study of the Indochina Region R. Chhin & S. Yoden https://doi.org/10.1029/2017JD028026
64. Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East Y. Khosravi et al. https://doi.org/10.1038/s41612-025-01033-9
65. Advances in aggregation methods for multi-model ensembles in climate science: A comprehensive review M. Shiru et al. https://doi.org/10.1016/j.pce.2026.104391
66. Drought projection in the Indochina Region based on the optimal ensemble subset of CMIP5 models R. Chhin et al. https://doi.org/10.1007/s10584-020-02850-y
67. Advanced climate modeling frameworks: state-of-the-art techniques, uncertainties, and the principle of responsibility J. Chahed https://doi.org/10.1007/s40808-025-02525-6
68. The Optimal Multimodel Ensemble of Bias-Corrected CMIP5 Climate Models over China X. Yang et al. https://doi.org/10.1175/JHM-D-19-0141.1
69. Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3 J. Lee et al. https://doi.org/10.5194/gmd-17-3919-2024
70. Implications of climate model selection for projections of decision-relevant metrics: A case study of chill hours in California K. Jagannathan et al. https://doi.org/10.1016/j.cliser.2020.100154
71. Review of approaches for selection and ensembling of GCMs K. Raju & D. Kumar https://doi.org/10.2166/wcc.2020.128
72. Emergent constraints on climate sensitivities M. Williamson et al. https://doi.org/10.1103/RevModPhys.93.025004
73. Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications D. Dieng et al. https://doi.org/10.1029/2021JD034836
74. Model spread and progress in climate modelling J. Jebeile & A. Barberousse https://doi.org/10.1007/s13194-021-00387-0
75. Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble G. Di Virgilio et al. https://doi.org/10.5194/gmd-18-671-2025
76. Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia B. Wang et al. https://doi.org/10.1002/joc.5705
77. Future changes in Aridity Index at two and four degrees of global warming above preindustrial levels X. Wang et al. https://doi.org/10.1002/joc.6620
78. Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide A. Elshall et al. https://doi.org/10.3389/feart.2022.786223
79. Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
80. Robust historical evapotranspiration trends across climate regimes S. Hobeichi et al. https://doi.org/10.5194/hess-25-3855-2021
81. Multi-Model Ensemble Machine Learning Approaches to Project Climatic Scenarios in a River Basin in the Pyrenees N. Bilbao-Barrenetxea et al. https://doi.org/10.1007/s41748-024-00408-x
82. Multi-Model Ensemble Enhances the Spatiotemporal Comprehensive Performance of Regional Climate in China Y. Wang et al. https://doi.org/10.3390/rs17040582
83. Evaluating NEX-GDDP-CMIP6 Models at Four Stations in Northern Togo: Seasonal-Cycle Skill, Ranking Robustness and Ensemble Selection K. Kotchadjo et al. https://doi.org/10.3390/cli14110243
84. Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change N. Herger et al. https://doi.org/10.1007/s00382-019-04690-8
85. Taking climate model evaluation to the next level V. Eyring et al. https://doi.org/10.1038/s41558-018-0355-y
86. Establishing a Range of Extreme Precipitation Estimates in California for Planning in the Face of Climate Change D. Feldman et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001410
87. A systematic analysis of climate model precipitation in southern Brazil M. Pereima et al. https://doi.org/10.1002/joc.7460
88. Range Expansion of the Giant Water Bug Lethocerus patruelis (Stål, 1854) in Europe A. Simoncini et al. https://doi.org/10.1002/ece3.72458
89. The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources A. Walker et al. https://doi.org/10.5194/gmd-11-3159-2018
90. Evaluating the skills of the CMIP5 global climate models using multicriteria decision-making analysis in Ethiopia E. Tesfaye et al. https://doi.org/10.1016/j.heliyon.2023.e20320
91. Statistical Modeling to Predict Climate Change Effects on Watershed Scale Evapotranspiration R. Khanal et al. https://doi.org/10.3390/atmos12121565
92. Similarities within a multi-model ensemble: functional data analysis framework E. Holtanová et al. https://doi.org/10.5194/gmd-12-735-2019
93. Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling A. Mezghani et al. https://doi.org/10.1175/JAMC-D-18-0179.1
94. Sustainability of Arctic Ecosystems in a Changing Climate O. Anisimov & E. Zhil’tsova https://doi.org/10.3103/S1068373922050077
95. Can We Use 1D Models to Predict 3D Model Response to Forcing in an Idealized Framework? Y. Hwong et al. https://doi.org/10.1029/2021MS002785
96. Sign of Observed California Temperature Trends Depends on Data Set Homogenization: Implications for Weighting and Downscaling A. Charn et al. https://doi.org/10.1029/2022GL099186
97. An Optimization-Based Approach to Developing Computationally Efficient Long-Term Event-Loss Scenario Ensembles J. Wang et al. https://doi.org/10.1061/AJRUA6.RUENG-2004
98. Representing climate evolution in ensembles of GCM simulations for the Hudson Bay System M. Braun et al. https://doi.org/10.1525/elementa.2021.00011
99. Informing Future Risks of Record‐Level Rainfall in the United States B. Sanderson et al. https://doi.org/10.1029/2019GL082362
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101. Evaluation and Projection of Degree-Days and Degree-Days Categories in Southeast Europe Using EURO-CORDEX H. Chervenkov & K. Slavov https://doi.org/10.3390/atmos16101153
102. Constraining CMIP6 Projections of an Ice‐Free Arctic Using a Weighting Scheme J. Zhao et al. https://doi.org/10.1029/2022EF002708
103. Population-Weighted Degree-Days over Southeast Europe—Near Past Climate Evaluation and Future Projections with NEX-GDDP CMIP6 Ensemble H. Chervenkov & K. Slavov https://doi.org/10.3390/cli13040066
104. Theoretical foundations of emergent constraints: relationships between climate sensitivity and global temperature variability in conceptual models M. Williamson et al. https://doi.org/10.1093/climsys/dzy006
105. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics K. Ahmed et al. https://doi.org/10.5194/hess-23-4803-2019
106. Assessing the Effect of Bias Correction Methods on the Development of Intensity–Duration–Frequency Curves Based on Projections from the CORDEX Central America GCM-RCM Multimodel-Ensemble M. Mendez et al. https://doi.org/10.3390/w16233473
107. Beyond the ordinary metrics on the evaluation of historical Euro-CORDEX simulations over Türkiye: the mutual information approach S. Vazifehkhah et al. https://doi.org/10.1007/s00704-023-04492-3
108. Impact of surface temperature biases on climate change projections of the South Pacific Convergence Zone C. Dutheil et al. https://doi.org/10.1007/s00382-019-04692-6
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110. NEX-GDDP Multimodel Ensemble vs. E-OBS—Evaluation of the Extreme Temperatures and Precipitation over Southeast Europe: Historical Comparison H. Chervenkov & K. Slavov https://doi.org/10.3390/atmos13040581
111. Integrating climate model ensembles for reliable regional drought assessment through redundancy control H. Abbas et al. https://doi.org/10.1002/qj.70211
112. An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles A. Merrifield et al. https://doi.org/10.5194/esd-11-807-2020
113. Assessing the Robustness of Future Extreme Precipitation Intensification in the CMIP5 Ensemble M. Bador et al. https://doi.org/10.1175/JCLI-D-17-0683.1
114. Observational uncertainty in the added value of regional climate modelling over Australia H. Imran & J. Evans https://doi.org/10.1007/s00382-024-07562-y
115. Ensembles for Viticulture Climate Classifications of the Willamette Valley Wine Region B. Skahill et al. https://doi.org/10.3390/cli9090140
116. Possibility of Stabilizing the Greenland Ice Sheet X. Wang et al. https://doi.org/10.1029/2021EF002152
117. Future Wind Conditions in the German Bight Under RCP8.5 Emissions Scenario (2006–2099) From Regional Coupled Ocean–Atmosphere Model System MPIOM‐REMO M. Ortega et al. https://doi.org/10.1002/joc.8814
118. Climate Projections for Pinot Noir Ripening Potential in the Fort Ross-Seaview, Los Carneros, Petaluma Gap, and Russian River Valley American Viticultural Areas B. Skahill et al. https://doi.org/10.3390/agronomy13030696