149 citations as recorded by crossref.

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19. Comparison of Three Techniques to Adjust Daily Precipitation Biases from Regional Climate Models over Germany C. Ibebuchi et al. https://doi.org/10.3390/w14040600
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27. Analysis of Precipitation Projections over the Climate Gradient of the Arkansas Red River Basin L. Qiao et al. https://doi.org/10.1175/JAMC-D-16-0201.1
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37. 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
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42. Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach F. Hanzer et al. https://doi.org/10.5194/hess-22-1593-2018
43. Bias nonstationarity of global climate model outputs: The role of internal climate variability and climate model sensitivity Y. Hui et al. https://doi.org/10.1002/joc.5950
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45. Nonparametric quantile mapping using the response surface method – bias correction of daily precipitation T. Bong et al. https://doi.org/10.2166/wcc.2017.127
46. When will meteorological droughts become the new normal climatic condition in Central-Southern Chile? F. González-Leiva et al. https://doi.org/10.1080/02626667.2026.2625979
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48. Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa B. Ayugi et al. https://doi.org/10.3390/w12030801
49. Designing ecological climate change impact assessments to reflect key climatic drivers H. Sofaer et al. https://doi.org/10.1111/gcb.13653
50. Evaluation of some distributional downscaling methods as applied to daily maximum temperature with emphasis on extremes J. Lanzante et al. https://doi.org/10.1002/joc.6288
51. Comparison of CMIP3 and CMIP5 projected hydrologic conditions over the Upper Colorado River Basin J. Ayers et al. https://doi.org/10.1002/joc.4594
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53. 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
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55. Statistical downscaling differences strongly alter projected climate damages S. Miller et al. https://doi.org/10.1038/s43247-025-02134-2
56. 1950–2100 climate trends in avalanche activity in Haute-Maurienne, French Alps F. Doussot et al. https://doi.org/10.5194/nhess-26-4383-2026
57. Impact of climate change on landslides frequency: the Esino river basin case study (Central Italy) L. Sangelantoni et al. https://doi.org/10.1007/s11069-018-3328-6
58. Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble H. Eum & A. Cannon https://doi.org/10.1002/joc.4924
59. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models M. Turco et al. https://doi.org/10.1038/s41467-018-06358-z
60. Responses of Unimpaired Flows, Storage, and Managed Flows to Scenarios of Climate Change in the San Francisco Bay‐Delta Watershed N. Knowles et al. https://doi.org/10.1029/2018WR022852
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65. Trends in projections of standardized precipitation indices in a future climate in Poland M. Osuch et al. https://doi.org/10.5194/hess-20-1947-2016
66. Bayesian Bias Correction of Satellite Rainfall Estimates for Climate Studies M. Kimani et al. https://doi.org/10.3390/rs10071074
67. Quantifying future rainfall extremes in Türkiye: a CMIP6 ensemble approach with statistical downscaling T. Tugrul et al. https://doi.org/10.1007/s11600-025-01551-3
68. Global Bias-Corrected CORDEX Datasets at Half Degree Resolution F. Yakubu et al. https://doi.org/10.1038/s41597-025-06200-4
69. Global warming intensifies once-in-a-decade extreme precipitation in summer in China A. Zhou & C. Yuan https://doi.org/10.1016/j.aosl.2024.100561
70. Evaluation of Quantile Delta Mapping as a bias‐correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil) A. Xavier et al. https://doi.org/10.1002/joc.7238
71. Bias adjustment and downscaling of snow cover fraction projections from regional climate models using remote sensing for the European Alps M. Matiu & F. Hanzer https://doi.org/10.5194/hess-26-3037-2022
72. 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
73. The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal A. Gobiet et al. https://doi.org/10.5194/hess-19-4055-2015
74. Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes A. Lutz et al. https://doi.org/10.1371/journal.pone.0165630
75. Extreme precipitation events under climate change in the Iberian Peninsula S. Cardoso Pereira et al. https://doi.org/10.1002/joc.6269
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77. Assessing future changes in hydroclimatic processes in the Metropolitan Region of Belo Horizonte, Brazil, with the expanded Bluecat framework L. Santos et al. https://doi.org/10.3389/frwa.2025.1541052
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79. Potential Changes in Runoff of California’s Major Water Supply Watersheds in the 21st Century M. He et al. https://doi.org/10.3390/w11081651
80. Impacts of drought, food security policy and climate change on performance of irrigation schemes in Sub-saharan Africa: The case of Sudan S. Ahmed https://doi.org/10.1016/j.agwat.2020.106064
81. Future Projections of Heat Mortality Risk for Major European Cities A. Karwat & C. Franzke https://doi.org/10.1175/WCAS-D-20-0142.1
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107. Uncertainty Assessment in Drought Severities for the Cheongmicheon Watershed Using Multiple GCMs and the Reliability Ensemble Averaging Method P. Abdulai & E. Chung https://doi.org/10.3390/su11164283
108. State-of-the-art bias correction of climate models misrepresent climate science and misinform adaptation V. Chandel et al. https://doi.org/10.1088/1748-9326/ad6d82
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116. Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula A. Ugolotti et al. https://doi.org/10.3389/fenvs.2023.1116429
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120. Understanding Differences in California Climate Projections Produced by Dynamical and Statistical Downscaling D. Walton et al. https://doi.org/10.1029/2020JD032812
121. Climate Change Effects on Rainfall Intensity–Duration–Frequency (IDF) Curves for the Lake Erie Coast Using Various Climate Models S. Mainali & S. Sharma https://doi.org/10.3390/w15234063
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124. Modeling crop yields amidst climate change in the Nile basin (2040–2079) S. Ahmed https://doi.org/10.1007/s40808-021-01199-0
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128. Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes M. Switanek et al. https://doi.org/10.5194/hess-21-2649-2017
129. The Effect of Climate Change on Forest Fire Danger and Severity in the Canadian Boreal Forests for the Period 1976–2100 Y. Wang https://doi.org/10.1029/2023JD039118
130. On the Use of Original and Bias-Corrected Climate Simulations in Regional-Scale Hydrological Scenarios in the Mediterranean Basin L. Sangelantoni et al. https://doi.org/10.3390/atmos10120799
131. Global Warming Impacts on Severe Drought Characteristics in Asia Monsoon Region J. Kim et al. https://doi.org/10.3390/w12051360
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135. Global Bias-Corrected CORDEX Dataset at Quarter Degree Resolution F. Yakubu et al. https://doi.org/10.1038/s41597-026-08060-y
136. Extreme Climate Event Changes in China in the 1.5 and 2 °C Warmer Climates: Results From Statistical and Dynamical Downscaling D. Li et al. https://doi.org/10.1029/2018JD028835
137. Evaluation of the performance of Euro-CORDEX Regional Climate Models for assessing hydrological climate change impacts in Great Britain: A comparison of different spatial resolutions and quantile mapping bias correction methods E. Pastén-Zapata et al. https://doi.org/10.1016/j.jhydrol.2020.124653
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141. Future Summer Marine Heatwaves in the Western South Atlantic N. Costa & R. Rodrigues https://doi.org/10.1029/2021GL094509
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145. Projections of suitable cultivation area for major fruit trees and climate‐type in South Korea under representative concentration pathway scenarios using the ensemble of high‐resolution regional climate models E. Kim et al. https://doi.org/10.1002/joc.8102
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