91 citations as recorded by crossref.

1. 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
2. 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
3. Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.scitotenv.2022.158615
4. Advancing multivariate bias correction with copulas: a robust approach for preserving rank correlation in climate model accuracy G. Adutwum & E. Chung https://doi.org/10.1007/s00704-025-05504-0
5. The issue of properly ordering climate indices calculation and bias correction before identifying spatial analogs for agricultural applications P. Grenier et al. https://doi.org/10.1016/j.cliser.2019.100122
6. The impact of using different probability representations in application of equidistant quantile matching for bias adjustment of daily precipitation over the Daqing River Basin, North China M. Lv et al. https://doi.org/10.1002/joc.7272
7. 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
8. The effect of univariate bias adjustment on multivariate hazard estimates J. Zscheischler et al. https://doi.org/10.5194/esd-10-31-2019
9. A quantile–quantile adjustment of the EURO‐CORDEX projections for temperatures and precipitation M. Cardell et al. https://doi.org/10.1002/joc.5991
10. Projections of Future Climate Change in Singapore Based on a Multi-Site Multivariate Downscaling Approach X. Li et al. https://doi.org/10.3390/w11112300
11. 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
12. Multivariate bias corrections of climate simulations: which benefits for which losses? B. François et al. https://doi.org/10.5194/esd-11-537-2020
13. Climate change impacts on regional fire weather in heterogeneous landscapes of central Europe J. Miller et al. https://doi.org/10.5194/nhess-24-411-2024
14. 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
15. Evolution of Mediterranean extreme dry spells during the wet season under climate change F. Raymond et al. https://doi.org/10.1007/s10113-019-01526-3
16. Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment M. Widmann et al. https://doi.org/10.1002/joc.6024
17. Lake Malawi’s threshold behaviour: A stakeholder-informed model to simulate sensitivity to climate change A. Bhave et al. https://doi.org/10.1016/j.jhydrol.2020.124671
18. Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments S. Galmarini et al. https://doi.org/10.1016/j.agsy.2023.103846
19. 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
20. 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
21. Bias Correction for Climate Model Output (1) Method Characteristics and Classification S. WATANABE https://doi.org/10.3178/jjshwr.33.243
22. 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
23. Future projections of summer Tibetan Plateau temperature based on the combined influence of sea surface temperature and soil moisture T. Zhang et al. https://doi.org/10.1038/s41612-026-01428-2
24. The Effects of Climate Change on Meteorological and Hydrological Drought in the Harşit River Basin (Türkiye) G. Demiral et al. https://doi.org/10.54365/adyumbd.1963232
25. Using artificial intelligence to identify CMIP6 models from daily SLP maps P. Yiou & S. Thao https://doi.org/10.1038/s41612-025-01246-y
26. 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
27. 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
28. 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
29. Changes in temperature–precipitation correlations over Europe: are climate models reliable? M. Vrac et al. https://doi.org/10.1007/s00382-022-06436-5
30. Characterizing and avoiding physical inconsistency generated by the application of univariate quantile mapping on daily minimum and maximum temperatures over Hudson Bay M. Agbazo & P. Grenier https://doi.org/10.1002/joc.6432
31. Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. https://doi.org/10.3389/feart.2021.673495
32. 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
33. Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models P. Hess et al. https://doi.org/10.1029/2023EF004002
34. 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
35. Identifying major phases in the use of land, energy and changing landscapes by agrarian societies (7,000 cal BP-Present) in Cantabrian Spain, based on cultural changes and anthropogenic signals A. Martinez et al. https://doi.org/10.3389/fearc.2024.1339172
36. Physics-constrained deep learning bias correction of CMIP6 solar radiation over Africa and its implications for solar power planning in a changing climate P. Adigun et al. https://doi.org/10.1016/j.renene.2025.124658
37. DASH: a MATLAB toolbox for paleoclimate data assimilation J. King et al. https://doi.org/10.5194/gmd-16-5653-2023
38. Multi-site multivariate downscaling of global climate model outputs: an integrated framework combining quantile mapping, stochastic weather generator and Empirical Copula approaches X. Li & V. Babovic https://doi.org/10.1007/s00382-018-4480-0
39. To bias correct or not to bias correct? An agricultural impact modelers’ perspective on regional climate model data P. Laux et al. https://doi.org/10.1016/j.agrformet.2021.108406
40. Next-Generation Drought Intensity–Duration–Frequency Curves for Early Warning Systems in Ethiopia’s Pastoral Region G. Tegegne et al. https://doi.org/10.3390/cli13020031
41. 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
42. A physically constrained multivariate bias correction framework for projecting compound drought and heatwave risk in the Yangtze River Basin Y. Zhuo et al. https://doi.org/10.1016/j.jhydrol.2026.136475
43. 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
44. Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes P. Ear et al. https://doi.org/10.3390/atmos16040480
45. 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
46. Future projections of High Atlas snowpack and runoff under climate change A. Tuel et al. https://doi.org/10.5194/hess-26-571-2022
47. Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires https://doi.org/10.1007/s10584-023-03597-y
48. Seasonal circulation regimes in the North Atlantic: Towards a new seasonality F. Breton et al. https://doi.org/10.1002/joc.7565
49. A Two-Stage GMFAMM Approximation for Joint Bias Correction of NASA POWER Hydroclimatic Data: The ColClim Web Application D. Arango-Londoño et al. https://doi.org/10.3390/s26134301
50. 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
51. 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
52. Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments J. Meyer et al. https://doi.org/10.5194/hess-23-1339-2019
53. A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. https://doi.org/10.1017/eds.2024.42
54. Hunter-gatherer impact on European interglacial vegetation: A modelling approach A. Nikulina et al. https://doi.org/10.1016/j.quascirev.2023.108439
55. Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
56. 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
57. Development of localized climate projections for Vietnam’s Mekong Delta using univariate and multivariate bias corrections of CMIP6 simulations D. Dong Phuong et al. https://doi.org/10.1007/s00382-026-08347-1
58. Multivariate stochastic bias corrections with optimal transport Y. Robin et al. https://doi.org/10.5194/hess-23-773-2019
59. Application of a bivariate bias-correction approach to yield long-term attributes of Indian precipitation and temperature C. Gupta & R. Bhowmik https://doi.org/10.3389/fclim.2023.1067960
60. 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
61. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. https://doi.org/10.2166/wcc.2023.230
62. Impact of bias adjustment strategy on ensemble projections of hydrological extremes P. Astagneau et al. https://doi.org/10.5194/hess-29-5695-2025
63. Evaluating regional climate model simulations at Wadi El Abid catchment (northeastern Tunisia) using HBV rainfall-runoff model K. Djebbi & H. Dakhlaoui https://doi.org/10.1007/s12517-022-11160-9
64. START: A Hybrid Spatio-Temporal Attention ResNet Transformer for Explainable Multivariable Meteorological Bias-correction D. Singh et al. https://doi.org/10.1007/s41748-026-01132-4
65. 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
66. 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
67. Combining global climate models using graph cuts S. Thao et al. https://doi.org/10.1007/s00382-022-06213-4
68. A spatially adaptive multi-resolution generative algorithm: Application to simulating flood wave propagation J. Carreau & P. Naveau https://doi.org/10.1016/j.wace.2023.100580
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen https://doi.org/10.1007/s11430-023-1201-y
76. 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
77. 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
78. Bias correction of daily precipitation for ungauged locations using geostatistical approaches: A case study for the CORDEX‐Africa ensemble M. Lorenz et al. https://doi.org/10.1002/joc.7649
79. 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
80. Evaluating statistical and machine learning bias-correction methods for CMIP6 rainfall projections in a tropical megacity N. Miniandi et al. https://doi.org/10.1007/s00704-026-06223-w
81. 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
82. Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method M. Li et al. https://doi.org/10.1175/JAMC-D-19-0093.1
83. A new multi-site multi-variable stochastic model with inter-site and inter-variable correlations, low frequency attributes and stochasticity: A case study in the lower Yellow River basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2021.126365
84. Characteristics of the snow depth in 2020 and estimation of future changes based on the relationship between temperature and precipitation. S. SAITO et al. https://doi.org/10.2208/jscejer.76.5_I_261
85. 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
86. Impacts of land use land cover change and climate change on river hydro-morphology- a review of research studies in tropical regions N. Kayitesi et al. https://doi.org/10.1016/j.jhydrol.2022.128702
87. Effects of univariate and multivariate statistical downscaling methods on climatic and hydrologic indicators for Alberta, Canada H. Eum et al. https://doi.org/10.1016/j.jhydrol.2020.125065
88. Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling J. Van de Velde et al. https://doi.org/10.3390/w13111573
89. Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. https://doi.org/10.1016/j.jhydrol.2023.129807
90. Evaluating the impact of rainfall–runoff model structural uncertainty on the hydrological rating of regional climate model simulations H. Dakhlaoui & K. Djebbi https://doi.org/10.2166/wcc.2021.004
91. 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