235 citations as recorded by crossref.

1. Reducing uncertainty in local temperature projections S. Qasmi & A. Ribes https://doi.org/10.1126/sciadv.abo6872
2. 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
3. Expert judgment in climate science: How it is used and how it can be justified M. Majszak & J. Jebeile https://doi.org/10.1016/j.shpsa.2023.05.005
4. 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
5. 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
6. Projecting and valuing climate change impacts on anxiety and depression in the contiguous USA: a damage function approach A. Belova et al. https://doi.org/10.1016/j.lanplh.2025.101426
7. 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
8. Common Error Patterns in the Regional Atmospheric Circulation Simulated by the CMIP Multi‐Model Ensemble S. Brands https://doi.org/10.1029/2022GL101446
9. Sahel Rainfall Projections Constrained by Past Sensitivity to Global Warming J. Schewe & A. Levermann https://doi.org/10.1029/2022GL098286
10. Indian Ocean dynamic sea level, its variability and projections in CMIP6 models C. Sajidh & A. Chatterjee https://doi.org/10.1007/s00382-023-06676-z
11. Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework P. Nguyen et al. https://doi.org/10.5194/gmd-17-7285-2024
12. Emergent Constraints on CMIP6 Climate Warming Projections: Contrasting Cloud- and Surface Temperature–Based Constraints Y. Liang et al. https://doi.org/10.1175/JCLI-D-21-0468.1
13. Accounting for Pacific climate variability increases projected global warming Y. Liang et al. https://doi.org/10.1038/s41558-024-02017-y
14. Emergent Constraints on the Large-Scale Atmospheric Circulation and Regional Hydroclimate: Do They Still Work in CMIP6 and How Much Can They Actually Constrain the Future? I. Simpson et al. https://doi.org/10.1175/JCLI-D-21-0055.1
15. Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6 E. Holtanová et al. https://doi.org/10.3389/feart.2022.1018661
16. Validation and Selection of a Representative Subset from the Ensemble of EURO-CORDEX EUR11 Regional Climate Model Outputs for the Czech Republic J. Meitner et al. https://doi.org/10.3390/atmos14091442
17. Differential Credibility of Climate Modes in CMIP6 J. Coburn & S. Pryor https://doi.org/10.1175/JCLI-D-21-0359.1
18. Climate Model Projections for Canada: A Comparison of CMIP5 and CMIP6 S. Sobie et al. https://doi.org/10.1080/07055900.2021.2011103
19. Temperature Sensitivity of Freshwater Denitrification and N2O Emission—A Meta‐Analysis M. Velthuis & A. Veraart https://doi.org/10.1029/2022GB007339
20. Spatial Modeling and Future Projection of Extreme Precipitation Extents P. Zhong et al. https://doi.org/10.1080/01621459.2024.2408045
21. Fighting climate change: soil bacteria communities and topography play a role in plant colonization of desert areas X. Sun et al. https://doi.org/10.1111/1462-2920.15799
22. Australian climate warming: observed change from 1850 and global temperature targets M. Grose et al. https://doi.org/10.1071/ES22018
23. Evaluation for CMIP6 Climate Models Using a Weighting Technique: A Case Study in the Kherlen River Basin, Mongolia M. Zorigt et al. https://doi.org/10.1007/s00024-025-03832-w
24. Projections of fire emissions and the consequent impacts on air quality under 1.5 °C and 2 °C global warming C. Tian et al. https://doi.org/10.1016/j.envpol.2023.121311
25. Land-use change causes rapid carbon losses in Congo Basin peatlands across climate scenarios, whereas the effects of climate change alone are uncertain D. Young et al. https://doi.org/10.1098/rstb.2024.0486
26. Selecting CMIP6 models to assess the impact of climate change on energy need for heating and cooling in Europe H. Filahi et al. https://doi.org/10.1007/s00704-024-05263-4
27. Historical and Future Windstorms in the Northeastern United States S. Pryor et al. https://doi.org/10.3390/cli13050105
28. Towards annual updating of forced warming to date and constrained climate projections A. Ribes et al. https://doi.org/10.1038/s41467-025-63026-9
29. Bayesian estimates for changes of the Russian river runoff in the 21st century as based on the CMIP6 model ensemble simulations А. Medvedev et al. https://doi.org/10.31857/S0002351524020021
30. Selecting and weighting dynamical models using data-driven approaches P. Le Bras et al. https://doi.org/10.5194/npg-31-303-2024
31. Evaluation of CMIP6 models toward dynamical downscaling over 14 CORDEX domains M. Zhang et al. https://doi.org/10.1007/s00382-022-06355-5
32. Drivers of Recent North Pacific Decadal Variability: The Role of Aerosol Forcing A. Dittus et al. https://doi.org/10.1029/2021EF002249
33. Correlation Between Sea‐Level Rise and Aspects of Future Tropical Cyclone Activity in CMIP6 Models J. Lockwood et al. https://doi.org/10.1029/2021EF002462
34. Concerning prognostic estimations on hazardous weather events: a road to nowhere or to home? Y. Kolokolov & A. Monovskaya https://doi.org/10.1088/1755-1315/1070/1/012019
35. Pantropical Indo-Atlantic temperature gradient modulates multi-decadal AMOC variability in models and observations B. Ferster et al. https://doi.org/10.1038/s41612-023-00489-x
36. Dominant inflation of the Arctic Ocean’s Beaufort Gyre in a warming climate Q. Wang et al. https://doi.org/10.1038/s43247-025-02028-3
37. Model spread and progress in climate modelling J. Jebeile & A. Barberousse https://doi.org/10.1007/s13194-021-00387-0
38. Reducing Snow Amount Uncertainty in CMIP6 PanCanadian Climate Projections D. Matte et al. https://doi.org/10.1080/07055900.2025.2478835
39. Deep learning-driven statistical bias correction for climate risk assessment of projected temperature extremes in the Nordic region P. Loganathan et al. https://doi.org/10.1038/s44304-026-00207-6
40. How well have CMIP3, CMIP5 and CMIP6 future climate projections portrayed the recently observed warming D. Carvalho et al. https://doi.org/10.1038/s41598-022-16264-6
41. Climate response to off-equatorial stratospheric sulfur injections in three Earth system models – Part 1: Experimental protocols and surface changes D. Visioni et al. https://doi.org/10.5194/acp-23-663-2023
42. 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
43. Ensemble Numerical Experiments with Earth System Models A. Eliseev https://doi.org/10.1007/s11141-026-10472-0
44. Learning from weather and climate science to prepare for a future pandemic S. Schemm et al. https://doi.org/10.1073/pnas.2209091120
45. Machine learning to optimize climate projection over China with multi-model ensemble simulations T. Li et al. https://doi.org/10.1088/1748-9326/ac1d0c
46. Identifying climate models based on their daily output using machine learning L. Brunner & S. Sippel https://doi.org/10.1017/eds.2023.23
47. Evaluating regional climate change during 2021–2080 for Iran and neighboring countries (a comparative analysis of projections and reanalysis data) H. Dehban et al. https://doi.org/10.1007/s00704-025-05381-7
48. Temperature and Precipitation Change over South China in CMIP5 and CMIP6 Models: Historical Simulation and Future Projection D. Peng et al. https://doi.org/10.1007/s00376-024-4375-4
49. 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
50. Assessing observational constraints on future European climate in an out-of-sample framework C. O’Reilly et al. https://doi.org/10.1038/s41612-024-00648-8
51. 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
52. Bayesian Estimates of Changes in Russian River Runoff in the 21st Century Based on the CMIP6 Ensemble Model Simulations A. Medvedev et al. https://doi.org/10.1134/S000143382470018X
53. Optimizing climate model selection in regional studies using an adaptive weather type based framework: a case study for extreme heat in Belgium F. Serras et al. https://doi.org/10.1007/s00382-024-07432-7
54. Dynamical downscaling CMIP6 models over New Zealand: added value of climatology and extremes P. Gibson et al. https://doi.org/10.1007/s00382-024-07337-5
55. Uncertainties in projections of climate extremes indices in South America via Bayesian inference C. Gouveia et al. https://doi.org/10.1002/joc.7650
56. A global climate model ensemble for downscaled monthly climate normals over North America C. Mahony et al. https://doi.org/10.1002/joc.7566
57. Global warming increases risk from compound dry‐hot events to human and agricultural systems Y. Zhang et al. https://doi.org/10.1002/joc.8229
58. Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022 S. Nath et al. https://doi.org/10.1016/j.wace.2024.100671
59. Projecting Global Mean Sea‐Level Change Using CMIP6 Models T. Hermans et al. https://doi.org/10.1029/2020GL092064
60. Validation of key Arctic energy and water budget components in CMIP6 S. Winkelbauer et al. https://doi.org/10.1007/s00382-024-07105-5
61. Large potential of performance-based model weighting to improve decadal climate forecast skill V. Verjans et al. https://doi.org/10.1038/s41612-026-01397-6
62. A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs) M. Meinshausen et al. https://doi.org/10.5194/gmd-17-4533-2024
63. Projected changes and uncertainty in cold surges over northern China using the CMIP6 weighted multi-model ensemble S. Shuaifeng & Y. Xiaodong https://doi.org/10.1016/j.atmosres.2022.106334
64. Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future C. Lee et al. https://doi.org/10.1029/2023EF003539
65. Probabilistic projections of future warming and climate sensitivity trajectories P. Goodwin https://doi.org/10.1093/oxfclm/kgab007
66. FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration N. Leach et al. https://doi.org/10.5194/gmd-14-3007-2021
67. Combination of Decadal Predictions and Climate Projections in Time: Challenges and Potential Solutions D. Befort et al. https://doi.org/10.1029/2022GL098568
68. On the role of moist and dry processes in atmospheric blocking biases in the Euro-Atlantic region in CMIP6 E. Dolores-Tesillos et al. https://doi.org/10.5194/wcd-6-471-2025
69. Future Projections and Uncertainty Assessment of Precipitation Extremes in the Korean Peninsula from the CMIP6 Ensemble with a Statistical Framework Y. Shin et al. https://doi.org/10.3390/atmos12010097
70. Effects of Climate Change on Streamflow in the Ayazma River Basin in the Marmara Region of Turkey K. Seddiqe et al. https://doi.org/10.3390/w15040763
71. Assessing CMIP6 uncertainties at global warming levels G. Evin et al. https://doi.org/10.1007/s00382-024-07323-x
72. ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint V. Portmann et al. https://doi.org/10.5194/gmd-18-9015-2025
73. Future precipitation extremes over base Himalayan Uttarakhand region: analysis using the statistically downscaled, bias-corrected high-resolution NEX-GDDP datasets B. Vinodhkumar et al. https://doi.org/10.1007/s00704-022-04111-7
74. Constraining CMIP6 Projections of an Ice‐Free Arctic Using a Weighting Scheme J. Zhao et al. https://doi.org/10.1029/2022EF002708
75. Extreme weather and cascading photovoltaic power vulnerabilities under climate change P. Adigun et al. https://doi.org/10.1016/j.seta.2025.104776
76. A simple framework for likely climate projections applied to tropical width D. Baldassare & T. Reichler https://doi.org/10.1007/s00382-024-07335-7
77. Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates K. Chang et al. https://doi.org/10.1111/gcb.16755
78. Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study V. Shevchenko et al. https://doi.org/10.1109/ACCESS.2024.3358865
79. Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics J. Lockwood et al. https://doi.org/10.1029/2022JD037617
80. Projecting solar peak hours in southern Spain using temperature-based machine learning models until 2100 J. Bellido-Jiménez et al. https://doi.org/10.1016/j.apenergy.2026.127395
81. Changes of extreme precipitation in the Philippines, projected from the CMIP6 multi-model ensemble J. Hong et al. https://doi.org/10.1016/j.wace.2022.100480
82. Use of meta-heuristic approach in the estimation of aquifer's response to climate change under shared socioeconomic pathways N. Zeydalinejad & R. Dehghani https://doi.org/10.1016/j.gsd.2022.100882
83. A chance to “cure” local climate systems and reconcile humanity with Nature Y. Kolokolov & A. Monovskaya https://doi.org/10.1088/1755-1315/1045/1/012154
84. Substantial changes in the probability of future annual temperature extremes R. Slater et al. https://doi.org/10.1002/asl.1061
85. 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
86. Microplastics in global rivers: Sustainable practices Y. Guo et al. https://doi.org/10.1002/sd.3279
87. Simulations With the Marine Biogeochemistry Library (MARBL) M. Long et al. https://doi.org/10.1029/2021MS002647
88. Historical footprints and future projections of global dust burden from bias-corrected CMIP6 models J. Liu et al. https://doi.org/10.1038/s41612-023-00550-9
89. Human Versus Natural Influences on Climate and Biodiversity: The Carbon Dioxide Connection W. Davis https://doi.org/10.3390/sci7040152
90. Comparison of Climate Model Large Ensembles With Observations in the Arctic Using Simple Neural Networks Z. Labe & E. Barnes https://doi.org/10.1029/2022EA002348
91. Linking hydroclimate indices to projected warming temperature and increased precipitation under CMIP6 for a sub-arctic basin C. Nakigudde et al. https://doi.org/10.1016/j.ejrh.2024.102059
92. Assessment and Constraint of Mesozooplankton in CMIP6 Earth System Models C. Petrik et al. https://doi.org/10.1029/2022GB007367
93. Effects of multi-observations uncertainty and models similarity on climate change projections R. Pathak et al. https://doi.org/10.1038/s41612-023-00473-5
94. Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections F. Sperna Weiland et al. https://doi.org/10.3389/frwa.2021.713537
95. Future extreme precipitation may shift to colder seasons in northern mid- and high latitudes D. Zhu et al. https://doi.org/10.1038/s43247-025-02651-0
96. Change in Precipitation over the Tibetan Plateau Projected by Weighted CMIP6 Models Y. Zhao et al. https://doi.org/10.1007/s00376-022-1401-2
97. RECCAP2 Future Component: Consistency and Potential for Regional Assessment to Constrain Global Projections C. Jones et al. https://doi.org/10.1029/2023AV001024
98. Climate change may reduce suitable habitat for freshwater fish in a tropical watershed L. Peluso et al. https://doi.org/10.1007/s10584-023-03526-z
99. Rarest rainfall events will see the greatest relative increase in magnitude under future climate change G. Gründemann et al. https://doi.org/10.1038/s43247-022-00558-8
100. Assessing high-resolution precipitation extremes in Central Asia: evaluation and future projections S. Gummadi et al. https://doi.org/10.1007/s10584-025-03872-0
101. Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections Z. Nicholls et al. https://doi.org/10.1029/2020EF001900
102. Multiobjective Optimization-Based Subjective Probability Distribution Aggregation: A Survey Z. Chen et al. https://doi.org/10.1109/TSMC.2026.3685096
103. 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
104. Securing the future of Colombian cacao: projected suitability changes and adaptation strategies under climate change T. Fremout et al. https://doi.org/10.1007/s10113-026-02549-3
105. MESMER-M: an Earth system model emulator for spatially resolved monthly temperature S. Nath et al. https://doi.org/10.5194/esd-13-851-2022
106. Future Changes in Various Cold Surges over China in CMIP6 Projections L. Ma et al. https://doi.org/10.1007/s00376-023-3220-5
107. Assessment of Equilibrium Climate Sensitivity of the Community Earth System Model Version 2 Through Simulation of the Last Glacial Maximum J. Zhu et al. https://doi.org/10.1029/2020GL091220
108. Observational constraints project a ~50% AMOC weakening by the end of this century V. Portmann et al. https://doi.org/10.1126/sciadv.adx4298
109. Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques S. Velivelli et al. https://doi.org/10.1007/s00704-024-05146-8
110. Evaluating the phase evolution of CMIP GCMs for agricultural climate-change impact assessments in China L. Yao et al. https://doi.org/10.1016/j.agrformet.2024.110282
111. A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to-high latitudes S. Brands https://doi.org/10.5194/gmd-15-1375-2022
112. Dynamic simulation of land use change and habitat quality assessment under climate change scenarios in Nanchang, China Z. Chen et al. https://doi.org/10.1007/s11356-023-31304-y
113. Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning M. Sonnewald & R. Lguensat https://doi.org/10.1029/2021MS002496
114. 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. https://doi.org/10.5194/gmd-15-2085-2022
115. A Novel Statistical Framework for Assessing Future Drought Using Multiple Global Climate Model: The Weighted Multimodal Adaptive Standardized Precipitation Index R. Fatima & Z. Ali https://doi.org/10.1007/s00024-025-03768-1
116. Future climate change implications in Bhutan from a downscaled and bias‐adjusted CMIP6 multimodel ensemble F. Lehner et al. https://doi.org/10.1002/joc.8623
117. 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
118. Assessing the impacts of climate change on photovoltaic energy generation in Colombia using 3-hourly CMIP6 projections C. Ramírez-Ruiz et al. https://doi.org/10.1016/j.egycc.2026.100266
119. The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India S. Shetty et al. https://doi.org/10.1002/joc.8131
120. Impact of multi-scenario land-use changes on habitat quality evolution in the Yangtze River economic belt B. Dong et al. https://doi.org/10.3389/fenvs.2024.1516703
121. Evaluation of global teleconnections in CMIP6 climate projections using complex networks C. Dalelane et al. https://doi.org/10.5194/esd-14-17-2023
122. Climate-aware decision-making: lessons for electric grid infrastructure planning and operations A. Brockway et al. https://doi.org/10.1088/1748-9326/ac7815
123. Multi-scenario assessment of future landslide susceptibility and relative exposure burden under Shared Socioeconomic Pathways in the Wumeng Mountains Region, Southwest China X. Huang et al. https://doi.org/10.1080/19475705.2026.2729954
124. Assessing the impact of global warming on windstorms in the northeastern United States using the pseudo-global-warming method J. Sethunadh et al. https://doi.org/10.1007/s11069-023-05968-1
125. Change of global land extreme temperature in the future X. Zhang et al. https://doi.org/10.1016/j.gloplacha.2024.104583
126. Constraining uncertainty in projected precipitation over land with causal discovery K. Debeire et al. https://doi.org/10.5194/esd-16-607-2025
127. Intensified changes in extreme climate events over the Philippines region under future climate scenarios S. Ignacio-Reardon et al. https://doi.org/10.1007/s40808-025-02581-y
128. A Novel Precipitation‐Concentration Kriging Ensemble Drought Assessment Framework (PCK‐EDAF) for Spatially Consistent and Reliable Drought Projection Under CMIP6 Climate Scenarios R. Fatima & Z. Ali https://doi.org/10.1002/joc.70588
129. The Mediterranean climate change hotspot in the CMIP5 and CMIP6 projections J. Cos et al. https://doi.org/10.5194/esd-13-321-2022
130. Comparing CMIP6 Climate Model Simulations of Annual Global Mean Temperatures to a New Combined Data Product P. Craigmile & P. Guttorp https://doi.org/10.1029/2022EA002468
131. Development of a new hybrid ensemble method for accurate characterization of future drought using multiple global climate models M. Yousaf et al. https://doi.org/10.1007/s00477-023-02526-w
132. Emergence of climate change signal in CMIP6 extreme indices N. Schuhen et al. https://doi.org/10.5194/nhess-26-753-2026
133. A novel procedure for monitoring temperature characteristics using multiple climate projections R. Khalil & Z. Ali https://doi.org/10.1007/s00477-025-03031-y
134. Three decades of simulating global temperature patterns with coupled global climate models L. Brunner et al. https://doi.org/10.1038/s43247-026-03497-w
135. Global patterns and future dynamics of belowground net primary productivity M. Li et al. https://doi.org/10.1016/j.ecolind.2026.115436
136. Indian summer monsoon simulations in successive generations of the NCAR Community Atmosphere Model R. Kumar et al. https://doi.org/10.1007/s00704-023-04514-0
137. RIME-X v1.0: combining simple climate models, Earth system models, and climate impact models into a unified statistical emulator for regional climate indicators N. Schwind et al. https://doi.org/10.5194/gmd-19-6797-2026
138. Evaluating observed and future spatiotemporal changes in precipitation and temperature across China based on CMIP6‐GCMs K. Lu et al. https://doi.org/10.1002/joc.7673
139. CMIP6 Earth System Models Project Greater Acceleration of Climate Zone Change Due To Stronger Warming Rates A. Bayar et al. https://doi.org/10.1029/2022EF002972
140. 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
141. Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6 C. Tebaldi et al. https://doi.org/10.5194/esd-12-253-2021
142. Probabilistic projections of El Niño Southern Oscillation properties accounting for model dependence and skill R. Olson et al. https://doi.org/10.1038/s41598-022-26513-3
143. Robustness and uncertainty in drought trend projections across dryland and non-dryland regions under greenhouse warming L. Li et al. https://doi.org/10.1016/j.wace.2026.100924
144. Improvement in the skill of CMIP6 decadal hindcasts for extreme rainfall events over the Indian summer monsoon region G. Konda et al. https://doi.org/10.1038/s41598-023-48268-1
145. 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
146. The role of crop simulation modeling in assessing potential climate change impacts D. Timlin et al. https://doi.org/10.1002/agg2.20453
147. Assessing Climate Change Effects on Winter Wheat Production in the 3H Plain: Insights from Bias-Corrected CMIP6 Projections Y. Xu et al. https://doi.org/10.3390/agriculture14030469
148. Performance-based sub-selection of CMIP6 models for impact assessments in Europe T. Palmer et al. https://doi.org/10.5194/esd-14-457-2023
149. Plant Phenomics: The Force Behind Tomorrow’s Crop Phenotyping Tools P. Kumari et al. https://doi.org/10.1007/s00344-024-11450-4
150. Assessment of winter three-dimensional extreme cold events over the Northern Hemisphere during 1959–2020 based on CMIP6 models X. Wang et al. https://doi.org/10.1007/s00382-025-07831-4
151. Dispersal abilities favor commensalism in animal-plant interactions under climate change P. Lemes et al. https://doi.org/10.1016/j.scitotenv.2022.155157
152. An updated assessment of past and future warming over France based on a regional observational constraint A. Ribes et al. https://doi.org/10.5194/esd-13-1397-2022
153. Evolution of land surface feedbacks on extreme heat: Adapting existing coupling metrics to a changing climate P. Dirmeyer et al. https://doi.org/10.3389/fenvs.2022.949250
154. Screening CMIP6 models for Chile based on past performance and code genealogy F. Gateño et al. https://doi.org/10.1007/s10584-024-03742-1
155. The potential for structural errors in emergent constraints B. Sanderson et al. https://doi.org/10.5194/esd-12-899-2021
156. Emerging global risks of extreme rain-on-snow events under climate warming K. Hu et al. https://doi.org/10.1016/j.jhydrol.2026.135651
157. 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
158. Surface air temperature variability over India in CMIP6 models during spring and early summer: after effect of El Niño S. Velivelli et al. https://doi.org/10.1007/s00382-025-07654-3
159. Dynamic Response of Surface Water Temperature in Urban Lakes under Different Climate Scenarios—A Case Study in Dianchi Lake, China H. Duan et al. https://doi.org/10.3390/ijerph191912142
160. Scenarios of change in the realized climatic niche of mountain carnivores and ungulates C. Dragonetti et al. https://doi.org/10.1111/cobi.14035
161. Observation-based selection of climate models projects Arctic ice-free summers around 2035 D. Docquier & T. Koenigk https://doi.org/10.1038/s43247-021-00214-7
162. Opposite effects of climate and land‐use/cover change on Mediterranean herpetofauna: Insights from the southern Apennines D. Biancolini et al. https://doi.org/10.1111/acv.12999
163. network-based constraint to evaluate climate sensitivity L. Ricard et al. https://doi.org/10.1038/s41467-024-50813-z
164. Projected Changes and Uncertainty of Cold Surges Over Northern China Using the Cmip6 Weighted Multi-Model Ensemble S. Song & X. Yan https://doi.org/10.2139/ssrn.4074639
165. Future temperature and salinity in Puget Sound, Washington State, under CMIP6 climate change scenarios S. Walker et al. https://doi.org/10.2166/wcc.2022.282
166. Characterising the range and outliers in CMIP6 multi-model climate projections of extremes P. Pall & A. King https://doi.org/10.1088/1748-9326/ae293a
167. 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
168. A non-stationary climate-informed weather generator for assessing future flood risks V. Nguyen et al. https://doi.org/10.5194/ascmo-10-195-2024
169. Why we need lower-performance climate models R. O’Loughlin https://doi.org/10.1007/s10584-023-03661-7
170. Global spatio-temporal dynamics of Paeonia lactiflora: A dual-track framework linking natural distribution, production zones, and geo-authentic regions M. Zhang et al. https://doi.org/10.1016/j.indcrop.2026.124391
171. Changes in Temperature and Precipitation in Kazakhstan During XX–XXI-Centuries Based on Observational and CMIP6 Model Data D. Gushchina et al. https://doi.org/10.3390/cli14100212
172. Influence of groundwater recharge projections on climate-driven subsurface warming: insights from numerical modeling M. Tsypin et al. https://doi.org/10.5194/hess-30-1647-2026
173. Bias-corrected CMIP6 ensemble projections of temperature extremes and precipitation regimes in the Omo-Kuraz Watershed, Ethiopia: Implications for water security K. Erenso et al. https://doi.org/10.1016/j.envc.2026.101649
174. Vulnerability to climate change of cultivated and wild cacao in Ecuador V. Ceccarelli et al. https://doi.org/10.1007/s10584-024-03756-9
175. Cloud properties and their projected changes in CMIP models with low to high climate sensitivity L. Bock & A. Lauer https://doi.org/10.5194/acp-24-1587-2024
176. Present-day North Atlantic salinity constrains future warming of the Northern Hemisphere I. Park et al. https://doi.org/10.1038/s41558-023-01728-y
177. Projection of temperature and precipitation under climate change in Tabriz, Iran B. Ghazi & E. Jeihouni https://doi.org/10.1007/s12517-022-09848-z
178. Bayesian weighting of climate models based on climate sensitivity E. Massoud et al. https://doi.org/10.1038/s43247-023-01009-8
179. Using large ensembles of climate change mitigation scenarios for robust insights C. Guivarch et al. https://doi.org/10.1038/s41558-022-01349-x
180. Leveraging emergent constraints to reduce uncertainty in future compound drought and heatwave events across mainland China M. Wu et al. https://doi.org/10.1016/j.jhydrol.2025.133551
181. Increasing Fire Weather Potential Over Northeast China Linked to Declining Bering Sea Ice G. Liu et al. https://doi.org/10.1029/2023GL105931
182. Biogeochemical and Physical Assessment of CMIP5 and CMIP6 Ocean Components for the Southwest Pacific Ocean G. Rickard et al. https://doi.org/10.1029/2022JG007123
183. Filtering CMIP6 models in the Euro-Mediterranean based on a circulation patterns approach M. Olmo et al. https://doi.org/10.1016/j.wace.2025.100765
184. Large spread in the representation of compound long-duration dry and hot spells over Europe in CMIP5 C. Manning et al. https://doi.org/10.5194/wcd-4-309-2023
185. How fast is the frequency of precipitation extremes doubling in global land regions? G. Chagnaud et al. https://doi.org/10.1088/2515-7620/ad9f12
186. The need for carbon-emissions-driven climate projections in CMIP7 B. Sanderson et al. https://doi.org/10.5194/gmd-17-8141-2024
187. Integrated Assessment of Climate-Driven Streamflow Changes in a Transboundary Lake Basin Using CMIP6-SWAT+-BMA: A Sustainability Perspective F. Xiao et al. https://doi.org/10.3390/su17177901
188. Delivering probabilistic climate hazards assessments D. Huard et al. https://doi.org/10.1088/2515-7620/ae3a4d
189. Scenario choice impacts carbon allocation projection at global warming levels L. de Mora et al. https://doi.org/10.5194/esd-14-1295-2023
190. Model weighting for ISMIP6-Greenland based on observations and similarity among models X. Luo & S. Nowicki https://doi.org/10.1017/aog.2025.10010
191. An Assessment of the Oceanic Physical and Biogeochemical Components of CMIP5 and CMIP6 Models for the Ross Sea Region G. Rickard et al. https://doi.org/10.1029/2022JC018880
192. Model-Based Assessment of Phenological and Climate Suitability Dynamics for Winter Wheat in the 3H Plain Under Future Climate Scenarios Y. Xu et al. https://doi.org/10.3390/agriculture15151606
193. Enhancing global CMIP6 model temperature predictions using deep learning neural networks S. Velivelli et al. https://doi.org/10.1007/s11069-025-07491-x
194. Future sea-level projections with a coupled atmosphere-ocean-ice-sheet model J. Park et al. https://doi.org/10.1038/s41467-023-36051-9
195. Performance assessment of 22 CMIP6 climate models for projecting wind climate and wind power over the Mediterranean Sea E. Çalışır et al. https://doi.org/10.1007/s12665-026-12885-6
196. Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa B. Ayugi et al. https://doi.org/10.1002/joc.7207
197. Evaluation of Dynamically Downscaled CMIP6‐CCAM Models Over Australia S. Chapman et al. https://doi.org/10.1029/2023EF003548
198. Eddy activity in the Arctic Ocean projected to surge in a warming world X. Li et al. https://doi.org/10.1038/s41558-023-01908-w
199. Assessing Future Tropical Cyclone Risk Using Downscaled 1 CMIP6 Projections B. Fosu et al. https://doi.org/10.63024/dpva-2pa1
200. Confronting Earth System Model trends with observations I. Simpson et al. https://doi.org/10.1126/sciadv.adt8035
201. 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
202. 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
203. Atmospheric blocking representation in storm-resolving climate models under historical and future forcing E. Dolores-Tesillos et al. https://doi.org/10.5194/wcd-7-1089-2026
204. Brazil seed transfer zones: Supporting seed sourcing for climate‐resilient ecosystem restoration M. Silva et al. https://doi.org/10.1002/ppp3.70217
205. Observed and CMIP6 projected rainfall variability and change in drylands of southern Ethiopia A. Chinasho et al. https://doi.org/10.1371/journal.pclm.0000800
206. The Elo rating system as a new narrative for ranking climate models P. Monerie https://doi.org/10.1088/2515-7620/ae661c
207. 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
208. Description of historical and future projection simulations by the global coupled E3SMv1.0 model as used in CMIP6 X. Zheng et al. https://doi.org/10.5194/gmd-15-3941-2022
209. Future Extreme Precipitation in Summer Will Become More Widespread in China Depending on Level of Warming R. Yu et al. https://doi.org/10.1029/2022EF003413
210. The CMIP6-downscaled CORDEX-Southeast Asia (SEA) ensemble: evaluation and benchmarking for megacities of SEA P. Nguyen et al. https://doi.org/10.5194/gmd-19-7653-2026
211. Evolution of the Internal Climate Modes under Future Warming J. Coburn & S. Pryor https://doi.org/10.1175/JCLI-D-22-0200.1
212. A Comparison of Regional Climate Projections With a Range of Climate Sensitivities C. Barnes et al. https://doi.org/10.1029/2023JD038917
213. Running to warmer-drier springs in the Greater Mekong Subregion as climate warms Z. Dong et al. https://doi.org/10.1016/j.atmosres.2024.107606
214. Imminent loss of climate space for permafrost peatlands in Europe and Western Siberia R. Fewster et al. https://doi.org/10.1038/s41558-022-01296-7
215. Contribution of large-scale atmospheric circulation and anthropogenic aerosols to recent summer warming over western Europe H. Douville et al. https://doi.org/10.1007/s00382-025-07689-6
216. Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble J. Hong et al. https://doi.org/10.3390/atmos12081052
217. Projections of changes in ecosystem productivity under 1.5 °C and 2 °C global warming C. Tian et al. https://doi.org/10.1016/j.gloplacha.2021.103588
218. Evaluation of atmospheric circulations for dynamic downscaling in CMIP6 models over East Asia S. Song et al. https://doi.org/10.1007/s00382-022-06465-0
219. Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
220. Natural variability is a large source of uncertainty in future projections of hypoxia in the Baltic Sea H. Markus Meier et al. https://doi.org/10.1038/s43247-021-00115-9
221. Impacts of marine heatwaves on tropical western and central Pacific Island nations and their communities N. Holbrook et al. https://doi.org/10.1016/j.gloplacha.2021.103680
222. 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
223. Should we think of observationally constrained multidecade climate projections as predictions? T. Li et al. https://doi.org/10.1126/sciadv.adt6485
224. Projected shrinking of tropical vegetation biomass potential with future hydroclimatic shifts F. Meunier et al. https://doi.org/10.1088/1748-9326/ade81a
225. A novel statistical framework for the reduction of uncertainties in multimodel ensemble of global climate models of precipitation simulations R. Khalil & Z. Ali https://doi.org/10.1007/s00382-025-07867-6
226. New Potential to Reduce Uncertainty in Regional Climate Projections by Combining Physical and Socio‐Economic Constraints F. Lehner et al. https://doi.org/10.1029/2023AV000887
227. The ExtremeX global climate model experiment: investigating thermodynamic and dynamic processes contributing to weather and climate extremes K. Wehrli et al. https://doi.org/10.5194/esd-13-1167-2022
228. Temporal fragmentation of the energy demand in Europe: Impact of climate change on the maneuverability of energy system H. Filahi et al. https://doi.org/10.1016/j.cliser.2024.100469
229. Sustainable food systems and nutrition in the 21st century: a report from the 22nd annual Harvard Nutrition Obesity Symposium J. Fanzo et al. https://doi.org/10.1093/ajcn/nqab315
230. Prediction of trade-offs and synergies and analysis of driving forces of ecosystem services in Jiangxi, China Y. Chen et al. https://doi.org/10.1007/s10668-025-06333-x
231. Extreme climate changes over three major river basins in China as seen in CMIP5 and CMIP6 X. Zhu et al. https://doi.org/10.1007/s00382-021-05767-z
232. Observational constraints suggest a smaller effective radiative forcing from aerosol–cloud interactions C. Park et al. https://doi.org/10.5194/acp-25-7299-2025
233. Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis F. Lema et al. https://doi.org/10.5194/hess-29-1981-2025
234. Tropical and subtropical Asia's valued tree species under threat H. Gaisberger et al. https://doi.org/10.1111/cobi.13873
235. Pitfalls in diagnosing temperature extremes L. Brunner & A. Voigt https://doi.org/10.1038/s41467-024-46349-x