158 citations as recorded by crossref.

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25. Evaluating Nex‐GDDP CMIP6 Models for Extreme Wet and Dry Events Over Indonesia A. Kurniadi et al. https://doi.org/10.1002/joc.70437
26. Toward more robust net primary production projections in the North Atlantic Ocean S. Doléac et al. https://doi.org/10.5194/bg-22-841-2025
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28. Spatiotemporal Reliability Ensemble Averaging of Multimodel Simulations G. Tegegne et al. https://doi.org/10.1029/2019GL083053
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36. Exploring the Multiverse of Analytical Decisions in Scaling Educational Large-Scale Assessment Data: A Specification Curve Analysis for PISA 2018 Mathematics Data A. Robitzsch https://doi.org/10.3390/ejihpe12070054
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44. Precipitation regime change in Western North America: The role of Atmospheric Rivers A. Gershunov et al. https://doi.org/10.1038/s41598-019-46169-w
45. Constraining Uncertainties in CMIP5 Projections of September Arctic Sea Ice Extent with Observations D. Senftleben et al. https://doi.org/10.1175/JCLI-D-19-0075.1
46. Water scarcity down to earth surface in a Mediterranean climate: The extreme future of soil moisture in Portugal P. Soares & D. Lima https://doi.org/10.1016/j.jhydrol.2022.128731
47. A Markov chain method for weighting climate model ensembles M. Kulinich et al. https://doi.org/10.5194/gmd-14-3539-2021
48. 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
49. Influence of Environmental Factors on the Site Selection and Layout of Ancient Military Towns (Zhejiang Region) L. Tan et al. https://doi.org/10.3390/su14052572
50. Similarities within a multi-model ensemble: functional data analysis framework E. Holtanová et al. https://doi.org/10.5194/gmd-12-735-2019
51. Selecting and weighting dynamical models using data-driven approaches P. Le Bras et al. https://doi.org/10.5194/npg-31-303-2024
52. 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
53. Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions M. Castaneda-Gonzalez et al. https://doi.org/10.1007/s10584-023-03643-9
54. Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective P. Nowack & D. Watson-Parris https://doi.org/10.5194/acp-25-2365-2025
55. 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
56. Reducing Snow Amount Uncertainty in CMIP6 PanCanadian Climate Projections D. Matte et al. https://doi.org/10.1080/07055900.2025.2478835
57. The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation A. Wootten et al. https://doi.org/10.3390/cli8120138
58. Assessing Flooding from Changes in Extreme Rainfall: Using the Design Rainfall Approach in Hydrologic Modeling A. Jalowska et al. https://doi.org/10.3390/w17152228
59. Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence M. Amos et al. https://doi.org/10.5194/acp-20-9961-2020
60. 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
61. 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
62. 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
63. Why we need lower-performance climate models R. O’Loughlin https://doi.org/10.1007/s10584-023-03661-7
64. Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy C. Jones et al. https://doi.org/10.5194/esd-15-1319-2024
65. An Integrated Modeling Framework in Projections of Hydrological Extremes H. Meresa et al. https://doi.org/10.1007/s10712-022-09737-w
66. On the Choice of the Item Response Model for Scaling PISA Data: Model Selection Based on Information Criteria and Quantifying Model Uncertainty A. Robitzsch https://doi.org/10.3390/e24060760
67. Grappling with uncertainties in physical climate impact projections of water resources R. Dankers & Z. Kundzewicz https://doi.org/10.1007/s10584-020-02858-4
68. Impacts of climate change on the food-water nexus in central Arizona X. Guan & G. Mascaro https://doi.org/10.1016/j.agrformet.2023.109413
69. Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers E. Massoud et al. https://doi.org/10.1029/2019EF001249
70. Projected Changes to Hydroclimate Seasonality in the Continental United States K. Marvel et al. https://doi.org/10.1029/2021EF002019
71. Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models L. Suarez-Gutierrez & N. Maher https://doi.org/10.1038/s41467-025-67005-y
72. Projected wave climate temporal variability due to climate change A. Loarca et al. https://doi.org/10.1007/s00477-020-01946-2
73. Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? M. Schlund et al. https://doi.org/10.5194/esd-11-1233-2020
74. 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
75. Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States A. Wootten et al. https://doi.org/10.5194/esd-14-121-2023
76. Effective sample size for precipitation estimation in atmospheric general circulation model ensemble experiments: dependence on temporal and spatial averaging scales K. Hibino & I. Takayabu https://doi.org/10.1007/s10584-020-02886-0
77. 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
78. Projecting Response of Ecological Vulnerability to Future Climate Change and Human Policies in the Yellow River Basin, China X. Zhang et al. https://doi.org/10.3390/rs16183410
79. 21st Century alpine climate change S. Kotlarski et al. https://doi.org/10.1007/s00382-022-06303-3
80. Fighting big data and ensemble fatigue in climate change impact studies: Can we turn the ensemble cascade upside down? E. Van Uytven et al. https://doi.org/10.1002/joc.6696
81. An Approach for Selecting Observationally-Constrained Global Climate Model Ensembles for Regional Climate Impacts and Adaptation Studies in Canada D. Jeong & A. Cannon https://doi.org/10.1080/07055900.2023.2239194
82. 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
83. Future changes of socioeconomic exposure to potential landslide hazards over mainland China D. Li et al. https://doi.org/10.1016/j.wace.2024.100731
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85. Toward Consistent Observational Constraints in Climate Predictions and Projections G. Hegerl et al. https://doi.org/10.3389/fclim.2021.678109
86. Distribution-based pooling for combination and multi-model bias correction of climate simulations M. Vrac et al. https://doi.org/10.5194/esd-15-735-2024
87. Reducing uncertainty in local temperature projections S. Qasmi & A. Ribes https://doi.org/10.1126/sciadv.abo6872
88. Hydrological benefits of restoring wildfire regimes in the Sierra Nevada persist in a warming climate E. Rakhmatulina et al. https://doi.org/10.1016/j.jhydrol.2020.125808
89. Development of a Hoisting Safety Risk Framework Based on the STAMP Theory and PLS-SEM Method Y. Junjia et al. https://doi.org/10.1109/ACCESS.2024.3453929
90. 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
91. Inferences of Groundwater Response to Projected Hydroclimatic Changes in North Florida F. Gordu & M. Nachabe https://doi.org/10.1061/JHYEFF.HEENG-5827
92. 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
93. Warm-Season Precipitation in the Eastern Pamir Plateau: Evaluation from Multi-Source Datasets and Elevation Dependence M. Yao et al. https://doi.org/10.3390/rs17193302
94. Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles M. Zumwald et al. https://doi.org/10.1002/wcc.654
95. Constraining uncertainty in projected precipitation over land with causal discovery K. Debeire et al. https://doi.org/10.5194/esd-16-607-2025
96. Evaluating the sensitivity of robust water resource interventions to climate change scenarios R. Geressu et al. https://doi.org/10.1016/j.crm.2022.100442
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107. Wetter California Projected by CMIP6 Models With Observational Constraints Under a High GHG Emission Scenario F. Li et al. https://doi.org/10.1029/2022EF002694
108. Multidisciplinary assessment of the impact of temperature rise and headwind on aircraft take-off performance S. Salles et al. https://doi.org/10.1007/s10584-025-04016-0
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115. Limited comparability of global and local estimates of environmental flow requirements to sustain river ecosystems M. Messager et al. https://doi.org/10.1088/1748-9326/ad1cb5
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124. Unrevealing past and future vegetation restoration on the Loess Plateau and its impact on terrestrial water storage K. Liu et al. https://doi.org/10.1016/j.jhydrol.2022.129021
125. Sensitivity of future climate change and uncertainty over India to performance-based model weighting R. Singh & K. AchutaRao https://doi.org/10.1007/s10584-019-02643-y
126. Future extreme hourly wet bulb temperatures using downscaled climate model projections of temperature and relative humidity M. Alessi & A. DeGaetano https://doi.org/10.1007/s00704-020-03368-0
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128. Changes in temperature and precipitation in the instrumental period (1951–2018) and projections up to 2100 in Podgorica (Montenegro) D. Burić & M. Doderović https://doi.org/10.1002/joc.6671
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131. Probabilistic projection of extreme precipitation changes over Iran by the CMIP6 multi-model ensemble S. Khansalari & A. Mohammadi https://doi.org/10.1007/s10584-024-03771-w
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138. Comparison between statistical and dynamical downscaling for estimating significant wave height (Hsig) in the Cuban Archipelago A. Hidalgo-Mayo & I. Mitrani-Arenal https://doi.org/10.1007/s00704-026-06102-4
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143. Exploration of diverse solutions for the calibration of imperfect climate models S. Peatier et al. https://doi.org/10.5194/esd-15-987-2024
144. A surrogate weighted mean ensemble method to reduce the uncertainty at a regional scale for the calculation of potential evapotranspiration B. Yoo et al. https://doi.org/10.1038/s41598-020-57466-0
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149. Evaluation of Satellite and Reanalysis Precipitable Water Vapor Data Sets Against Radiosonde Observations in Central Asia J. Jiang et al. https://doi.org/10.1029/2019EA000654
150. An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment J. Dunne et al. https://doi.org/10.5194/gmd-18-6671-2025
151. Carbon Flux Variability From a Relatively Simple Ecosystem Model With Assimilated Data Is Consistent With Terrestrial Biosphere Model Estimates G. Quetin et al. https://doi.org/10.1029/2019MS001889
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154. Uncovering the shortcomings of a weather typing method E. Van Uytven et al. https://doi.org/10.5194/hess-24-2671-2020
155. Benchmarking Simulated Precipitation Variability Amplitude across Time Scales M. Ahn et al. https://doi.org/10.1175/JCLI-D-21-0542.1
156. Projected shrinking of tropical vegetation biomass potential with future hydroclimatic shifts F. Meunier et al. https://doi.org/10.1088/1748-9326/ade81a
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