112 citations as recorded by crossref.

1. Climate models underestimate the sensitivity of Arctic sea ice to carbon emissions F. Diebold & G. Rudebusch https://doi.org/10.1016/j.eneco.2023.107012
2. The distinct problems of physical inconsistency and of multivariate bias involved in the statistical adjustment of climate simulations M. Alavoine & P. Grenier https://doi.org/10.1002/joc.7878
3. Regional Copula Modeling of Rainfall Duration and Intensity: Derivation and Validation of IDF Curves in the Kastoria Basin E. Leivadiotis et al. https://doi.org/10.3390/hydrology13040117
4. Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. https://doi.org/10.5194/hess-26-1481-2022
5. Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100 Y. Quilcaille et al. https://doi.org/10.5194/essd-15-2153-2023
6. Future climate risk to UK agriculture from compound events F. Garry et al. https://doi.org/10.1016/j.crm.2021.100282
7. Assessing temperature and precipitation bias nonstationarity of CMIP6 global climate models over Iran N. Azad & A. Ahmadi https://doi.org/10.1007/s00704-024-05322-w
8. A Process-Conditioned and Spatially Consistent Method for Reducing Systematic Biases in Modeled Streamflow A. Bennett et al. https://doi.org/10.1175/JHM-D-21-0174.1
9. Regional sub-daily stochastic weather generator based on reanalyses for surface water stress estimation in central Tunisia N. Farhani et al. https://doi.org/10.1016/j.envsoft.2022.105448
10. Climate change impact on flood hazard over Italy M. García-Valdecasas Ojeda et al. https://doi.org/10.1016/j.jhydrol.2022.128628
11. MIdASv0.2.1 – MultI-scale bias AdjuStment P. Berg et al. https://doi.org/10.5194/gmd-15-6165-2022
12. CHIRPWeb, an Online Tool for Providing a Bias Corrected CHIRP Grid Dataset Using Field Measurements J. Pérez-Sánchez et al. https://doi.org/10.1007/s00024-026-03966-5
13. Attributing the occurrence and intensity of extreme events with the flow analogue method R. Noyelle et al. https://doi.org/10.5194/wcd-6-817-2025
14. Global climate model bias correction using deep learning A. Pasula & D. Subramani https://doi.org/10.1088/3049-4753/ade9c3
15. Assessing the impact of artificial snowmaking on Dagu Glacier variation: a case study from a tourism glacier Y. Xie et al. https://doi.org/10.1038/s41598-025-89722-6
16. How does bias correction impact simulated drought characteristics by Regional Climate Models? P. Nguyen-Ngoc-Bich et al. https://doi.org/10.1007/s10584-025-03901-y
17. Present and future interannual variability in wildfire occurrence: a large ensemble application to the United States T. Keeping et al. https://doi.org/10.3389/ffgc.2025.1519836
18. Hotspot movement of compound events on the Europe continent S. Doshi et al. https://doi.org/10.1038/s41598-023-45067-6
19. Growth of spatial statistics for agriculture and environment: The example of BioSP at INRAE D. Allard https://doi.org/10.1016/j.spasta.2025.100938
20. ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1) F. Spuler et al. https://doi.org/10.5194/gmd-17-1249-2024
21. Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin C. Romero-Hernández et al. https://doi.org/10.1016/j.jsames.2024.104898
22. Historical trends and future projections of annual rainfall from CMIP6 models in Ho Chi Minh City, Vietnam D. Phuong et al. https://doi.org/10.3354/cr01736
23. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. https://doi.org/10.1016/j.earscirev.2022.104241
24. 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
25. Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction J. Zhang et al. https://doi.org/10.1214/21-AOAS1460
26. Learning to Correct Climate Projection Biases B. Pan et al. https://doi.org/10.1029/2021MS002509
27. Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
28. An improved modelling chain for bias-adjusted high-resolution climate and hydrological projections for Norway S. Huang et al. https://doi.org/10.5194/gmd-19-4567-2026
29. A standardized index for assessing sub-monthly compound dry and hot conditions with application in China J. Li et al. https://doi.org/10.5194/hess-25-1587-2021
30. A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation M. Cantalejo et al. https://doi.org/10.1038/s41598-024-76848-2
31. Hydrological Modelling and Climate Adaptation under Changing Climate: A Review with a Focus in Sub-Saharan Africa V. Banda et al. https://doi.org/10.3390/w14244031
32. Towards an integrated system modeling of water scarcity with projected changes in climate and socioeconomic conditions S. Dehghani et al. https://doi.org/10.1016/j.spc.2022.07.023
33. 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
34. Differentiated regional impacts of future meteorological changes and anthropogenic emission control on PM2.5 concentrations in China Y. Jiang et al. https://doi.org/10.1016/j.horiz.2025.100151
35. Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures X. Gao et al. https://doi.org/10.1007/s11356-021-16066-9
36. Projected increase in global compound agricultural drought and hot events under climate change W. Shi et al. https://doi.org/10.1016/j.gloplacha.2025.104962
37. High-resolution projections of outdoor thermal stress in the twenty-first century: a Tasmanian case study B. Weeding et al. https://doi.org/10.1007/s00484-024-02622-8
38. Is time a variable like the others in multivariate statistical downscaling and bias correction? Y. Robin & M. Vrac https://doi.org/10.5194/esd-12-1253-2021
39. Towards more realistic climate model outputs: a multivariate bias correction based on zero-inflated vine copulas H. Funk et al. https://doi.org/10.1093/jrsssc/qlaf044
40. Bias adjustment to preserve changes in variability: the unbiased mapping of GCM changes C. Chadwick et al. https://doi.org/10.1080/02626667.2023.2201450
41. Projected changes of rain, sleet, and snowfall in Norway E. Kuya et al. https://doi.org/10.1080/00291951.2024.2360409
42. Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. https://doi.org/10.3389/feart.2021.673495
43. Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin C. Thakur et al. https://doi.org/10.1016/j.ejrh.2025.102443
44. R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resampling M. Vrac & S. Thao https://doi.org/10.5194/gmd-13-5367-2020
45. Multivariate bias correction of ERA5 climatic data for assessing climate-related road vulnerabilities in Nigeria A. Abdussalam et al. https://doi.org/10.1007/s44288-025-00151-4
46. Changes in temperature–precipitation correlations over Europe: are climate models reliable? M. Vrac et al. https://doi.org/10.1007/s00382-022-06436-5
47. Impact of diverse configuration in multivariate bias correction methods on large-scale hydrological modelling under climate change K. Ahn et al. https://doi.org/10.1016/j.jhydrol.2023.130406
48. Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective V. de Padua & K. Ahn https://doi.org/10.1016/j.jhydrol.2024.132102
49. Global evaluation of the “dry gets drier, and wet gets wetter” paradigm from a terrestrial water storage change perspective J. Xiong et al. https://doi.org/10.5194/hess-26-6457-2022
50. Evaluation of a Multivariate Statistical Downscaling Method over Canada's Largest Pacific Basin S. Sobie et al. https://doi.org/10.1080/07055900.2025.2478829
51. Multivariate bias corrections of CMIP6 model simulations of compound dry and hot events across China Y. Meng et al. https://doi.org/10.1088/1748-9326/ac8e86
52. A Stochastic Multisite Bias Correction Method for Hydro-Meteorological Impact Studies H. Liu et al. https://doi.org/10.3390/w17121807
53. Evaluating the dependence structure of compound precipitation and wind speed extremes J. Zscheischler et al. https://doi.org/10.5194/esd-12-1-2021
54. A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. https://doi.org/10.1017/eds.2024.42
55. Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model M. Switanek et al. https://doi.org/10.1002/joc.7581
56. Future Projections of EURO‐CORDEX Raw and Bias‐Corrected Daily Maximum Wind Speeds Over Scandinavia C. Michel & A. Sorteberg https://doi.org/10.1029/2022JD037953
57. Investigating the robustness of extreme precipitation super-resolution across climates L. Largeau et al. https://doi.org/10.1016/j.wace.2026.100885
58. Cooperative adaptive management of the Nile River with climate and socio-economic uncertainties M. Basheer et al. https://doi.org/10.1038/s41558-022-01556-6
59. Improving biome and climate modelling for a set of past climate conditions: evaluating bias correction using the CDF-t approach A. Zapolska et al. https://doi.org/10.1088/2752-5295/accbe2
60. Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian https://doi.org/10.1007/s00382-024-07406-9
61. Elevation dependency of snowfall changes under climate change over the Tibetan Plateau: Evidence from CMIP6 GCMs Y. Gao et al. https://doi.org/10.1016/j.atmosres.2024.107832
62. Evaluating Seasonal Forecast Models for Cambodia’s Northern Tonle Sap Basin L. Brigadier et al. https://doi.org/10.1007/s13143-025-00393-9
63. Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings D. Robertson et al. https://doi.org/10.1016/j.jhydrol.2023.129322
64. Novel multivariate quantile mapping methods for ensemble post-processing of medium-range forecasts K. Whan et al. https://doi.org/10.1016/j.wace.2021.100310
65. Towards a compound-event-oriented climate model evaluation: a decomposition of the underlying biases in multivariate fire and heat stress hazards R. Villalobos-Herrera et al. https://doi.org/10.5194/nhess-21-1867-2021
66. Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin R. Ansari et al. https://doi.org/10.5194/gmd-16-2055-2023
67. Selection of climate simulations for climate change impact studies: case study of the Souss watershed, Morocco M. Meliho et al. https://doi.org/10.1007/s00704-025-05353-x
68. Downscaling and uncertainty analysis of future concurrent long-duration dry and hot events in China Y. Yang & J. Tang https://doi.org/10.1007/s10584-023-03481-9
69. Climate Trends and Future Scenarios in Afghanistan: Implications for Greenhouse Gas Emissions, Renewable Energy Potential, and Sustainable Development N. Akhundzadah https://doi.org/10.3390/en19041067
70. Correction of ERA5 temperature and relative humidity biases by bivariate quantile mapping for contrail formation analysis K. Wolf et al. https://doi.org/10.5194/acp-25-157-2025
71. Climate data selection for multi-decadal wind power forecasts S. Morelli et al. https://doi.org/10.1088/1748-9326/adc01f
72. Assessing internal changes in the future structure of dry–hot compound events: the case of the Pyrenees M. Lemus-Canovas & J. Lopez-Bustins https://doi.org/10.5194/nhess-21-1721-2021
73. Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations L. Qiu et al. https://doi.org/10.5194/esd-14-507-2023
74. Return Period of Nonconcurrent Climate Compound Events: A Nonparametric Bivariate Generalized Pareto Approach G. Jacquemin et al. https://doi.org/10.1002/env.70063
75. Assessing multivariate bias corrections of climate simulations on various impact models under climate change D. Allard et al. https://doi.org/10.5194/hess-29-4711-2025
76. Extending the global high-resolution downscaled projections dataset to include CMIP6 projections at increased resolution coherent with the ERA5-Land reanalysis T. Noël et al. https://doi.org/10.1016/j.dib.2022.108669
77. Should We Use Quantile-Mapping-Based Methods in a Climate Change Context? A “Perfect Model” Experiment M. Vrac et al. https://doi.org/10.3390/cli13070137
78. How the choice of model calibration procedure affects projections of lake surface water temperatures for future climatic conditions J. Napiorkowski et al. https://doi.org/10.1016/j.jhydrol.2025.133236
79. Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al. https://doi.org/10.1029/2023EF004002
80. From Climate Change to Design Change via the ΔEPW. A toolkit for Adaptive Urban Planning, Architecture, and Vegetation E. Naboni & M. Turrini https://doi.org/10.1016/j.buildenv.2026.114724
81. 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
82. Advancing research on compound weather and climate events via large ensemble model simulations E. Bevacqua et al. https://doi.org/10.1038/s41467-023-37847-5
83. A Large Ensemble Global Dataset for Climate Impact Assessments X. Gao et al. https://doi.org/10.1038/s41597-023-02708-9
84. Building the scientific basis for the adaptation strategy of an Alpine region: The ‘State of the climate in Trentino’ report A. Napoli et al. https://doi.org/10.1016/j.cliser.2025.100629
85. Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires https://doi.org/10.1007/s10584-023-03597-y
86. Comparison of bias correction methods in the arid region of Pakistan Z. Ali et al. https://doi.org/10.1088/1755-1315/1467/1/012026
87. Characterizing temperature and precipitation multi‐variate biases in 12 and 2.2 km UK Climate Projections F. Garry & D. Bernie https://doi.org/10.1002/joc.8006
88. Time of emergence of compound events: contribution of univariate and dependence properties B. François & M. Vrac https://doi.org/10.5194/nhess-23-21-2023
89. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. https://doi.org/10.2166/wcc.2023.230
90. Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations Y. Kim et al. https://doi.org/10.1029/2020GL092058
91. Approximating the Internal Variability of Bias-Corrected Global Temperature Projections with Spatial Stochastic Generators W. Hu & S. Castruccio https://doi.org/10.1175/JCLI-D-21-0083.1
92. Quantile-based bias-correction of extreme rainfall: Pros & cons of popular methods for climate signal preservation R. Padulano et al. https://doi.org/10.1016/j.jhydrol.2025.132814
93. Data driven deep learning for correcting global climate model projections of sea surface temperature and dynamic sea level in the Bay of Bengal A. Pasula & D. Subramani https://doi.org/10.1007/s00382-026-08063-w
94. Selecting representative climate models for climate change impact studies: case study of the Tanger-Tetouan-Al Hoceima, Morocco M. Qacami et al. https://doi.org/10.3389/fclim.2026.1743573
95. Ensemble bias correction of climate simulations: preserving internal variability P. Vaittinada Ayar et al. https://doi.org/10.1038/s41598-021-82715-1
96. Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain D. Quesada‐Chacón et al. https://doi.org/10.1029/2023EF003531
97. Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment C. Ngoungue Langue et al. https://doi.org/10.5194/wcd-2-893-2021
98. Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation F. Pons & D. Faranda https://doi.org/10.5194/ascmo-8-155-2022
99. Combining global climate models using graph cuts S. Thao et al. https://doi.org/10.1007/s00382-022-06213-4
100. Time Variability Correction of CMIP6 Climate Change Projections Y. Shao et al. https://doi.org/10.1029/2023MS003640
101. Seasonal circulation regimes in the North Atlantic: Towards a new seasonality F. Breton et al. https://doi.org/10.1002/joc.7565
102. Intensity and dynamics of extreme cold spells of the 21st century in France from CMIP6 data C. Cadiou & P. Yiou https://doi.org/10.5194/esd-16-1759-2025
103. Future change in amplitude and timing of high-flow events in a Canadian subarctic watershed O. Champagne et al. https://doi.org/10.1016/j.coldregions.2023.103807
104. Performance of weather research forecasting model for seasonal prediction of precipitation over Indonesian maritime continent I. Sofiati et al. https://doi.org/10.1016/j.kjs.2024.100293
105. On deep learning-based bias correction and downscaling of multiple climate models simulations F. Wang & D. Tian https://doi.org/10.1007/s00382-022-06277-2
106. Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. https://doi.org/10.5194/hess-26-2319-2022
107. 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
108. Climate change impacts on the Chiffa basin (northern Algeria) using bias-corrected RCM data A. Madani et al. https://doi.org/10.3389/frwa.2024.1507961
109. Multivariate bias correction of regional climate model boundary conditions Y. Kim et al. https://doi.org/10.1007/s00382-023-06718-6
110. Analysis of the 2024 Hajj heat event and future temperature extremes in Mecca G. Zittis et al. https://doi.org/10.1038/s44304-025-00159-3
111. Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction J. Martins et al. https://doi.org/10.3390/agronomy11050990
112. Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts D. Gergel et al. https://doi.org/10.5194/gmd-17-191-2024