141 citations as recorded by crossref.

1. Attribution of Extreme Rainfall Events in the South of France Using EURO‐CORDEX Simulations L. Luu et al. https://doi.org/10.1029/2018GL077807
2. Improving NCMRWF Unified Model rainfall forecasts over India through IMERG-calibrated IMDAA reanalysis A. Raghuvanshi et al. https://doi.org/10.1080/02626667.2026.2737818
3. Evaluation and projection of precipitation and temperature in a coastal climatic transitional zone in China based on CMIP6 GCMs X. Li et al. https://doi.org/10.1007/s00382-023-06781-z
4. Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods L. Guo et al. https://doi.org/10.1007/s10584-020-02841-z
5. Bivariate bias correction of the regional climate model ensemble over the Adriatic region R. Sokol Jurković et al. https://doi.org/10.1002/joc.7564
6. The VALUE perfect predictor experiment: Evaluation of temporal variability D. Maraun et al. https://doi.org/10.1002/joc.5222
7. A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model J. Wang et al. https://doi.org/10.1007/s00376-019-9091-0
8. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. https://doi.org/10.2166/wcc.2023.230
9. Evaluation of the ability of the Weather Research and Forecasting model to reproduce a sub-daily extreme rainfall event in Beijing, China using different domain configurations and spin-up times Q. Chu et al. https://doi.org/10.5194/hess-22-3391-2018
10. A spatial hybrid approach for downscaling of extreme precipitation fields A. Bechler et al. https://doi.org/10.1002/2014JD022558
11. Hydrologic Impacts of Surface Elevation and Spatial Resolution in Statistical Correction Approaches: Case Study of Flumendosa Basin, Italy E. Perra et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001969
12. Harmful Ostreopsis cf. ovata blooms could extend in time span with climate change in the Western Mediterranean Sea S. Fabri-Ruiz et al. https://doi.org/10.1016/j.scitotenv.2024.174726
13. 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
14. High-Resolution Downscaled CMIP6 Projections dataset of Key Climate Variables for Senegal A. Mbengue et al. https://doi.org/10.1038/s41597-026-07059-9
15. Bias Correction for Climate Model Output (1) Method Characteristics and Classification S. WATANABE https://doi.org/10.3178/jjshwr.33.243
16. Impact of Climate Change in West Africa on Cereal Production Per Capita in 2050 D. Defrance et al. https://doi.org/10.3390/su12187585
17. New Statistical Methods for Precipitation Bias Correction Applied to WRF Model Simulations in the Antisana Region, Ecuador M. Heredia et al. https://doi.org/10.1175/JHM-D-18-0032.1
18. Convection-Permitting Regional Climate Change Simulations for Understanding Future Climate and Informing Decision-Making in Africa C. Senior et al. https://doi.org/10.1175/BAMS-D-20-0020.1
19. Unravelling the Impacts of Climate Variability on Surface Runoff in the Mouhoun River Catchment (West Africa) C. Zouré et al. https://doi.org/10.3390/land12112017
20. Changes in Future Synoptic Circulation Patterns: Consequences for Extreme Event Attribution D. Faranda et al. https://doi.org/10.1029/2020GL088002
21. Impact of climate change on high wind and solar optimal mixes and system costs: the case of France J. Delort Ylla et al. https://doi.org/10.5194/adgeo-65-159-2025
22. Ensemble bias correction of climate simulations: preserving internal variability P. Vaittinada Ayar et al. https://doi.org/10.1038/s41598-021-82715-1
23. A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments T. Noël et al. https://doi.org/10.1016/j.dib.2021.106900
24. Climate Change Trends for the Urban Heat Island Intensities in Two Major Portuguese Cities C. Andrade et al. https://doi.org/10.3390/su15053970
25. 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
26. Climatic impacts on water resources in a tropical catchment in Uganda and adaptation measures proposed by resident stakeholders B. Mehdi et al. https://doi.org/10.1007/s10584-021-02958-9
27. Influence of Bias Correcting Predictors on Statistical Downscaling Models M. Vrac & P. Vaittinada Ayar https://doi.org/10.1175/JAMC-D-16-0079.1
28. Assessing present and future risk of water damage using building attributes, meteorology, and topography* C. Heinrich-Mertsching et al. https://doi.org/10.1093/jrsssc/qlad043
29. Multidimensional analysis of climate-induced streamflow variability using CMIP6 data and advanced modeling techniques P. Hlaing et al. https://doi.org/10.1080/10106049.2025.2504362
30. 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
31. Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics D. Faranda et al. https://doi.org/10.1007/s00382-023-06794-8
32. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration B. Rhoné et al. https://doi.org/10.1038/s41467-020-19066-4
33. Assessing nonstationarity impacts for historical and projected extreme precipitation in Turkey R. Aziz & I. Yucel https://doi.org/10.1007/s00704-020-03503-x
34. 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
35. GeoDS (v.1.0): a simple Geographical DownScaling model for long-term precipitation data over complex terrains J. Brenner et al. https://doi.org/10.5194/gmd-19-1075-2026
36. IPSL-AID: generative diffusion models for climate downscaling from global to regional scales K. Kingston et al. https://doi.org/10.1017/eds.2026.10066
37. 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
38. Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis E. Flaounas et al. https://doi.org/10.1007/s00382-012-1606-7
39. Review of Surface Solar Radiation Projections in Bias-Corrected Euro-Cordex Regional Climate Models B. Bartók https://doi.org/10.2478/awutp-2018-0001
40. An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations Y. Yang et al. https://doi.org/10.1007/s00382-019-04809-x
41. New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid Q. Yuan et al. https://doi.org/10.1175/JAMC-D-19-0086.1
42. Estimating IDF curves under changing climate conditions for different climate regions B. Gül & N. Kayaalp https://doi.org/10.2166/wcc.2023.306
43. Recurrence Spectra of European Temperature in Historical Climate Simulations M. Alvarez-Castro et al. https://doi.org/10.3390/atmos10040166
44. Comparison of projected water availability and demand reveals future hotspots of water stress in the Red River basin, USA H. Zamani Sabzi et al. https://doi.org/10.1016/j.ejrh.2019.100638
45. Modeling the Impact of Climate and Land Use/Land Cover Change on Water Availability in an Inland Valley Catchment in Burkina Faso M. Idrissou et al. https://doi.org/10.3390/hydrology9010012
46. Regional climate downscaling with prior statistical correction of the global climate forcing A. Colette et al. https://doi.org/10.1029/2012GL052258
47. Developing climate services for vulnerable islands in the Southwest Indian Ocean: A combined statistical and dynamical CMIP6 downscaling approach for climate change assessment M. Leroux et al. https://doi.org/10.1016/j.cliser.2024.100491
48. Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions B. Sebbar et al. https://doi.org/10.3390/atmos14040610
49. Design and validation of MEDRYS, a Mediterranean Sea reanalysis over the period 1992–2013 M. Hamon et al. https://doi.org/10.5194/os-12-577-2016
50. Benefits from using combined dynamical-statistical downscaling approaches – lessons from a case study in the Mediterranean region N. Guyennon et al. https://doi.org/10.5194/hess-17-705-2013
51. A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies A. Famien et al. https://doi.org/10.5194/esd-9-313-2018
52. Comparing the Performance of Dynamical and Statistical Downscaling on Historical Run Precipitation Data over a Semi-Arid Region N. Salehnia et al. https://doi.org/10.1007/s13143-019-00112-1
53. Assessing the impact of bias correction approaches on climate extremes and the climate change signal H. Zhang et al. https://doi.org/10.1002/met.2204
54. Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data T. Le Guenedal et al. https://doi.org/10.5194/gmd-15-8001-2022
55. Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* M. Vrac & P. Friederichs https://doi.org/10.1175/JCLI-D-14-00059.1
56. Conditional and residual trends of singular hot days in Europe A. Jézéquel et al. https://doi.org/10.1088/1748-9326/ab76dd
57. The effects of climate change-induced cooling demand on power grids R. Kapeller et al. https://doi.org/10.1016/j.egyr.2023.07.028
58. Hydrological Impact Assessment of Climate Change on Lake Tana’s Water Balance, Ethiopia Z. Nigatu et al. https://doi.org/10.4236/ajcc.2016.51005
59. 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
60. Sampling from a Max‐Stable Process Conditional on a Homogeneous Functional with an Application for Downscaling Climate Data M. Oesting et al. https://doi.org/10.1111/sjos.12299
61. Daytime-only mean data enhance understanding of land–atmosphere coupling Z. Yin et al. https://doi.org/10.5194/hess-27-861-2023
62. More than agriculture: Analysing time-cumulative human impact on European land-cover of second half of the Holocene A. Zapolska et al. https://doi.org/10.1016/j.quascirev.2023.108227
63. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment J. Gutiérrez et al. https://doi.org/10.1002/joc.5462
64. High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation of the ALADIN-Climate model in Morocco Y. Tramblay et al. https://doi.org/10.5194/hess-17-3721-2013
65. 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
66. 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
67. Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction M. Vrac https://doi.org/10.5194/hess-22-3175-2018
68. Rethinking future flood hazard: Hourly data challenge daily flood projections in Alpine catchments P. Astagneau et al. https://doi.org/10.1126/sciadv.aed6012
69. Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions A. Foughali et al. https://doi.org/10.3390/cli3030459
70. Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network B. Huang et al. https://doi.org/10.1088/1748-9326/ad66e6
71. Comparison of conventional and machine learning methods for bias correcting CMIP6 rainfall and temperature in Nigeria B. Tanimu et al. https://doi.org/10.1007/s00704-024-04888-9
72. Reconstructing High-Resolution Coastal Water Quality Data Based on a Deep Learning Multivariate Downscaling Approach X. Liu et al. https://doi.org/10.3390/rs18091346
73. Future changes in climate indices relevant to agriculture in the Aegean islands (Greece) G. Kitsara et al. https://doi.org/10.1007/s41207-020-00233-4
74. RCGAN: A generative adversarial framework for multi-variable bias correction in CMIP6 climate projections J. Liu et al. https://doi.org/10.1016/j.envsoft.2026.107171
75. Surface Wind-Speed Statistics Modelling: Alternatives to the Weibull Distribution and Performance Evaluation P. Drobinski et al. https://doi.org/10.1007/s10546-015-0035-7
76. Conditional simulations of the extremal t process: Application to fields of extreme precipitation A. Bechler et al. https://doi.org/10.1016/j.spasta.2015.04.003
77. Climate variability and trends in downscaled high-resolution simulations and projections over Metropolitan France R. Vautard et al. https://doi.org/10.1007/s00382-012-1621-8
78. Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean B. Grouillet et al. https://doi.org/10.5194/hess-20-1031-2016
79. Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter M. Vrac et al. https://doi.org/10.1002/2015JD024511
80. Impacts of Greenland and Antarctic Ice Sheet melt on future Köppen climate zone changes simulated by an atmospheric and oceanic general circulation model D. Defrance et al. https://doi.org/10.1016/j.apgeog.2020.102216
81. Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables P. Jones et al. https://doi.org/10.5194/essd-9-471-2017
82. Multivariate bias corrections of climate simulations: which benefits for which losses? B. François et al. https://doi.org/10.5194/esd-11-537-2020
83. The role of spatial scale in drought monitoring and early warning systems: a review J. Mardian https://doi.org/10.1139/er-2021-0102
84. Impact of regional climate change on the mosquito vector Aedes albopictus in a tropical island environment: La Réunion K. Lamy et al. https://doi.org/10.1016/j.scitotenv.2023.162484
85. Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model P. Fall et al. https://doi.org/10.3390/tropicalmed8060310
86. 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
87. Evaluation and improvement of tail behaviour in the cumulative distribution function transform downscaling method J. Lanzante et al. https://doi.org/10.1002/joc.5964
88. A complete hydro-climate model chain to investigate the influence of sea surface temperature on recent hydroclimatic variability in subtropical South America (Laguna Mar Chiquita, Argentina) M. Troin et al. https://doi.org/10.1007/s00382-015-2676-0
89. 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
90. Using bias-correction to improve future projections of offshore wind energy resource: A case study on the Iberian Peninsula X. Costoya et al. https://doi.org/10.1016/j.apenergy.2020.114562
91. Towards a Process-Informed Framework for Assessing the Credibility of Statistical and Dynamical Downscaling Methods M. Bukovsky et al. https://doi.org/10.3390/cli14020031
92. Toward robust fine-scale decadal precipitation forecasts through dynamically consistent subsampling J. Couallier et al. https://doi.org/10.5194/esd-17-1177-2026
93. Scaling of precipitation extremes with temperature in the French Mediterranean region: What explains the hook shape? P. Drobinski et al. https://doi.org/10.1002/2015JD023497
94. Projections of extreme rainfall and floods in Mediterranean basins from an ensemble of convection-permitting models N. Poncet et al. https://doi.org/10.1007/s10584-025-03983-8
95. Assessment of changes in return levels of historical and projected high and low flows of upper Euphrates basin in Turkey using nonstationary models R. Aziz & I. Yucel https://doi.org/10.1007/s10661-023-11136-6
96. Dynamically downscaled 1-km climate dataset for the Balearic Islands under present and future conditions Y. Bahuet-Bourret et al. https://doi.org/10.1038/s41597-026-07301-4
97. Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods E. Flaounas et al. https://doi.org/10.1007/s00382-012-1558-y
98. Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations P. Vaittinada Ayar et al. https://doi.org/10.1007/s00382-015-2647-5
99. A climate projection dataset tailored for the European energy sector B. Bartók et al. https://doi.org/10.1016/j.cliser.2019.100138
100. Projected Changes in the Atmospheric Dynamics of Climate Extremes in France P. Yiou et al. https://doi.org/10.3390/atmos12111440
101. Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. https://doi.org/10.1016/j.isprsjprs.2023.12.011
102. Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks B. François et al. https://doi.org/10.1007/s00382-021-05869-8
103. An Artificial Intelligence-Driven Precipitation Downscaling Method Using Spatiotemporally Coupled Multi-Source Data C. Li et al. https://doi.org/10.3390/atmos16111226
104. Climate and hydrological models to assess the impact of climate change on hydrological regime: a review R. Kour et al. https://doi.org/10.1007/s12517-016-2561-0
105. Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
106. The comparative influence of climate change and demography in spatially distributed degree-day projections for France in the 21st century S. Cros et al. https://doi.org/10.1016/j.cliser.2024.100539
107. Landward Perspective of Coastal Eutrophication Potential Under Future Climate Change: The Seine River Case (France) M. Raimonet et al. https://doi.org/10.3389/fmars.2018.00136
108. Assessing the level of adaptation to heat waves in Parisian housing L. Roccamena et al. https://doi.org/10.1080/14733315.2025.2530261
109. Assessing the impact of climate change on Combined Sewer Overflows based on small time step future rainfall timeseries and long-term continuous sewer network modelling F. Gogien et al. https://doi.org/10.1016/j.watres.2022.119504
110. Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods? N. Poncet et al. https://doi.org/10.5194/nhess-24-1163-2024
111. Heat waves analysis over France in present and future climate: Application of a new method on the EURO-CORDEX ensemble G. Ouzeau et al. https://doi.org/10.1016/j.cliser.2016.09.002
112. Modelling the effect of passive cooling measures on future energy needs for the Austrian building stock L. Mayrhofer et al. https://doi.org/10.1016/j.enbuild.2023.113333
113. Enhancing Understanding of the Impact of Climate Change on Malaria in West Africa Using the Vector-Borne Disease Community Model of the International Center for Theoretical Physics (VECTRI) and the Bias-Corrected Phase 6 Coupled Model Intercomparison Project Data (CMIP6) P. Fall et al. https://doi.org/10.3390/microbiolres14040145
114. Extreme Precipitation and Flood Estimation Using Large Ensemble Climate Model Outputs: A Review T. Tanaka et al. https://doi.org/10.1061/JHYEFF.HEENG-6665
115. How could climate change affect the magnitude, duration and frequency of hydrological droughts and floods in West Africa during the 21st century? A storyline approach J. Ekolu et al. https://doi.org/10.1016/j.jhydrol.2025.133482
116. Expanding CMCC seasonal prediction system v3.5 applications to the local scale through statistical downscaling techniques L. Aragão et al. https://doi.org/10.1016/j.atmosres.2026.108811
117. Consequences of rapid ice sheet melting on the Sahelian population vulnerability D. Defrance et al. https://doi.org/10.1073/pnas.1619358114
118. New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations K. Chun et al. https://doi.org/10.1038/s41612-026-01424-6
119. Hydrological simulation of Po River (North Italy) discharge under climate change scenarios using the RCM COSMO-CLM R. Vezzoli et al. https://doi.org/10.1016/j.scitotenv.2015.03.096
120. Impacts of climate change on sub-regional electricity demand and distribution in the southern United States M. Allen et al. https://doi.org/10.1038/nenergy.2016.103
121. Bias correction and projection of surface air temperature in LMDZ multiple simulation over central and eastern China L. Guo et al. https://doi.org/10.1016/j.accre.2018.02.003
122. Extreme precipitation in the Netherlands: An event attribution case study J. Eden et al. https://doi.org/10.1016/j.wace.2018.07.003
123. Increasing flood hazard in the Lower Mississippi River due to extreme storm clustering Y. Liu et al. https://doi.org/10.1126/sciadv.adt1868
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125. Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach A. Doury et al. https://doi.org/10.1007/s00382-022-06343-9
126. Changes in temperature–precipitation correlations over Europe: are climate models reliable? M. Vrac et al. https://doi.org/10.1007/s00382-022-06436-5
127. Attributing icing precipitations trend (1951-2098) in the context of climate change in Europe F. Raymond et al. https://doi.org/10.1051/climat/202321001
128. New insights from the bias-corrected simulations of CMIP6 in Northern Hemisphere’s snow drought Y. Hu et al. https://doi.org/10.1038/s43247-026-03187-7
129. 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
130. A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. https://doi.org/10.1017/eds.2024.42
131. Adjusting climate model bias for agricultural impact assessment: How to cut the mustard S. Galmarini et al. https://doi.org/10.1016/j.cliser.2019.01.004
132. Statistical downscaling for climate change projections in the Mediterranean region: methods and results J. Jacobeit et al. https://doi.org/10.1007/s10113-014-0605-0
133. Nonstationary stochastic rain type generation: accounting for climate drivers L. Benoit et al. https://doi.org/10.5194/hess-24-2841-2020
134. Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China Y. Xiang et al. https://doi.org/10.3390/agronomy14010181
135. 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
136. AI/ML-Enhanced Wind Forecasts for Reducing Uncertainty in Prescribed Fire Planning S. Brambilla et al. https://doi.org/10.3390/atmos17030312
137. A combined statistical bias correction and stochastic downscaling method for precipitation C. Volosciuk et al. https://doi.org/10.5194/hess-21-1693-2017
138. 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
139. Robust assessment of the time of emergence of precipitation change in West Africa M. Gaetani et al. https://doi.org/10.1038/s41598-020-63782-2
140. Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study R. Bonnet et al. https://doi.org/10.1088/2752-5295/ac6adc
141. The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models D. Verfaillie et al. https://doi.org/10.5194/gmd-10-4257-2017