136 citations as recorded by crossref.

1. The spatio-temporal trend of climate and characterization of drought in Borana Zone, Ethiopia S. Fanta et al. https://doi.org/10.1038/s41598-025-87345-5
2. Evaluation of four bias correction methods and random forest model for climate change projection in the Mara River Basin, East Africa P. das et al. https://doi.org/10.2166/wcc.2022.299
3. Hybrid approach in statistical bias correction of projected precipitation for the frequency analysis of extreme events M. Um et al. https://doi.org/10.1016/j.advwatres.2016.05.021
4. Assessment of rainfall bias correction techniques for improved hydrological simulation U. Ghimire et al. https://doi.org/10.1002/joc.5959
5. Improving Future Estimation of Cheliff-Mactaa-Tafna Streamflow via an Ensemble of Bias Correction Approaches M. Renima et al. https://doi.org/10.3390/cli10080123
6. 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
7. Propagation of biases in climate models from the synoptic to the regional scale: Implications for bias adjustment N. Addor et al. https://doi.org/10.1002/2015JD024040
8. A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates Q. Ma et al. https://doi.org/10.3390/rs11111345
9. Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
10. Decomposing satellite-based rainfall errors in flood estimation: Hydrological responses using a spatiotemporal object-based verification method M. Laverde-Barajas et al. https://doi.org/10.1016/j.jhydrol.2020.125554
11. Impact of deep learning-driven precipitation corrected data using near real-time satellite-based observations and model forecast in an integrated hydrological model K. Patakchi Yousefi et al. https://doi.org/10.3389/frwa.2024.1439906
12. Projected changes in flood indices in selected catchments in Poland in the 21st century M. Osuch et al. https://doi.org/10.1007/s00477-016-1296-5
13. Assessing the reliability of the Oued El Makhazine reservoir under climate change, irrigation expansion and inter-reservoir transfers: A WEAP-based study in northern Morocco N. Oumara & L. Youssfi https://doi.org/10.1016/j.envc.2026.101604
14. Yields and water footprints of sunflower and winter wheat under Different Climate Projections S. Yeşilköy & L. Şaylan https://doi.org/10.1016/j.jclepro.2021.126780
15. Increasing drought in Jordan: Climate change and cascading Syrian land-use impacts on reducing transboundary flow D. Rajsekhar & S. Gorelick https://doi.org/10.1126/sciadv.1700581
16. An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances S. Kusangaya et al. https://doi.org/10.1016/j.gloplacha.2016.07.014
17. A new bias-correction method for precipitation over complex terrain suitable for different climate states: a case study using WRF (version 3.8.1) P. Velasquez et al. https://doi.org/10.5194/gmd-13-5007-2020
18. Evaluation of multiple climate data sources for managing environmental resources in East Africa S. Gebrechorkos et al. https://doi.org/10.5194/hess-22-4547-2018
19. Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability C. Gao et al. https://doi.org/10.5194/hess-24-3251-2020
20. Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa S. Kusangaya et al. https://doi.org/10.1016/j.jhydrol.2018.01.017
21. Bias correction of climate models for hydrological modelling – are simple methods still useful? M. Shrestha et al. https://doi.org/10.1002/met.1655
22. Variable resolution modeling of near future mean temperature changes in the dry sub-humid region of Ghana E. Bessah et al. https://doi.org/10.1007/s40808-018-0479-0
23. Inter-comparisons and applicability of CMIP5 GCMs, RCMs and statistically downscaled NEX-GDDP based precipitation in India V. Singh et al. https://doi.org/10.1016/j.scitotenv.2019.134163
24. 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
25. Climate change effects on indicators of high and low river flow across Great Britain A. Kay et al. https://doi.org/10.1016/j.advwatres.2021.103909
26. Comparison of Future Changes in Frequency of Climate Extremes between Coastal and Inland Locations of Bengal Delta Based on CMIP6 Climate Models S. Das et al. https://doi.org/10.3390/atmos13111747
27. Intraseasonal scale ensemble forecasts of precipitation and evapotranspiration for the Madeira River basin using different physical parameterizations W. Gomes et al. https://doi.org/10.1016/j.atmosres.2022.106086
28. Impact of downscaled rainfall biases on projected runoff changes S. Charles et al. https://doi.org/10.5194/hess-24-2981-2020
29. Multisite bias correction of precipitation data from regional climate models J. Hnilica et al. https://doi.org/10.1002/joc.4890
30. Analysis of Precipitation and Temperature Trends Under the Impact of Climate Change Over Ten Districts of Jimma Zone, Ethiopia S. Fanta et al. https://doi.org/10.1007/s41748-022-00322-0
31. 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
32. Linking global climate change to local water availability: Limitations and prospects for a tropical mountain watershed D. González-Zeas et al. https://doi.org/10.1016/j.scitotenv.2018.09.309
33. Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers J. Hnilica et al. https://doi.org/10.5194/hess-23-1741-2019
34. Can the variability in precipitation simulations across GCMs be reduced through sensible bias correction? H. Nguyen et al. https://doi.org/10.1007/s00382-016-3510-z
35. Adaptation and application of the large LAERTES-EU regional climate model ensemble for modeling hydrological extremes: a pilot study for the Rhine basin F. Ehmele et al. https://doi.org/10.5194/nhess-22-677-2022
36. Prediction of Reservoir Sedimentation in the Long Term Period Due to the Impact of Climate Change: A Case Study of Pleikrong Reservoir X. Do et al. https://doi.org/10.20965/jdr.2022.p0552
37. A comparative assessment of climate change impacts on drought over Korea based on multiple climate projections and multiple drought indices M. Lee et al. https://doi.org/10.1007/s00382-018-4588-2
38. Projection of future precipitation extremes across the Bangkok Metropolitan Region R. Cooper https://doi.org/10.1016/j.heliyon.2019.e01678
39. Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand S. Wuthiwongtyohtin https://doi.org/10.2166/wcc.2020.021
40. Change-signal impacts in downscaled data and its influence on hydroclimate projections N. Potter et al. https://doi.org/10.1016/j.jhydrol.2018.06.018
41. The method of producing climate change datasets impacts the resulting policy guidance and chance of mal-adaptation M. Ekström et al. https://doi.org/10.1016/j.cliser.2016.09.003
42. Climate change impacts on regional agricultural irrigation water use in semi-arid environments X. Tian et al. https://doi.org/10.1016/j.agwat.2023.108239
43. Implication of bias correction on climate change impact projection of surface water resources in the Gidabo sub-basin, Southern Ethiopia A. Worako et al. https://doi.org/10.2166/wcc.2022.396
44. Precipitation Bias Correction: A Novel Semi‐parametric Quantile Mapping Method C. Rajulapati & S. Papalexiou https://doi.org/10.1029/2023EA002823
45. Climate Change over the Mediterranean Region: Local Temperature and Precipitation Variations at Five Pilot Sites V. Todaro et al. https://doi.org/10.3390/w14162499
46. Towards the development of bias-corrected rainfall erosivity time series for Europe F. Matthews et al. https://doi.org/10.1016/j.jhydrol.2024.132460
47. Probabilistic Flood Inundation Prediction Using Copula-Corrected Precipitation Forecast S. Unnithan et al. https://doi.org/10.1007/s11269-026-04894-8
48. A two‑stage deep learning framework for multi‑source precipitation fusion to optimize seasonal, extreme and precipitation event estimation S. Li et al. https://doi.org/10.1016/j.ejrh.2026.104139
49. Impacts of climate change on characteristics of daily‐scale rainfall events based on nine selected GCMs under four CMIP5 RCP scenarios in Qu River basin, east China C. Gao et al. https://doi.org/10.1002/joc.6246
50. Policy-Making toward Integrated Water Resources Management of Zarrine River Basin via System Dynamics Approach under Climate Change Impact A. Hosseini Baghanam et al. https://doi.org/10.3390/su14063376
51. Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica M. Mendez et al. https://doi.org/10.3390/w12020482
52. Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios F. Lehner et al. https://doi.org/10.5194/ascmo-9-29-2023
53. Surface runoff response to climate change based on artificial neural network (ANN) models: a case study with Zagunao catchment in Upper Minjiang River, Southwest China Y. Lin et al. https://doi.org/10.2166/wcc.2018.130
54. The New Bias Correction Method for Daily Extremes Precipitation over South Korea using CMIP6 GCMs Y. Song et al. https://doi.org/10.1007/s11269-022-03338-3
55. Modelling and Assessment of Climate Change Impact on Rainfed Rice Cultivation in a Sub-humid Subtropical Region A. Baishya et al. https://doi.org/10.1007/s40003-023-00671-w
56. 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
57. Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? P. Reiter et al. https://doi.org/10.1002/joc.5283
58. Bias Correction of Mixed Distributions of Temperature with Strong Diurnal Signal M. Haider et al. https://doi.org/10.1175/WAF-D-21-0108.1
59. Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin X. Ji et al. https://doi.org/10.1016/j.atmosres.2020.104964
60. A new region-aware bias-correction method for simulated precipitation in areas of complex orography J. Gómez-Navarro et al. https://doi.org/10.5194/gmd-11-2231-2018
61. Evaluation of WRF-chem simulations of NO2 and CO from biomass burning over East Africa and its surrounding regions R. Opio et al. https://doi.org/10.1007/s44195-022-00029-9
62. Assessment of Climate Change Impacts on Reservoir Storage Reliability, Resilience, and Vulnerability Using a Multivariate Frequency Bias Correction Approach H. Nguyen et al. https://doi.org/10.1029/2019WR026022
63. Modeling the atmospheric dispersion of SO2 from Mount Nyiragongo R. Opio et al. https://doi.org/10.1016/j.jafrearsci.2022.104771
64. Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations S. Ricard et al. https://doi.org/10.5194/hess-27-2375-2023
65. Assessment of bias correction methods for high resolution daily precipitation projections with CMIP6 models: A Canadian case study X. Li & Z. Li https://doi.org/10.1016/j.ejrh.2025.102223
66. Assessing future runoff changes with different potential evapotranspiration inputs based on multi-model ensemble of CMIP5 projections L. Shi et al. https://doi.org/10.1016/j.jhydrol.2022.128042
67. Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents Y. Song & E. Chung https://doi.org/10.5194/gmd-18-8017-2025
68. Impact of Climate Change on Water Resources in the Kilombero Catchment in Tanzania K. Näschen et al. https://doi.org/10.3390/w11040859
69. Bias-corrected regional climate projections of extreme rainfall in south-east Australia J. Evans et al. https://doi.org/10.1007/s00704-016-1949-9
70. The effect of GCM biases on global runoff simulations of a land surface model L. Papadimitriou et al. https://doi.org/10.5194/hess-21-4379-2017
71. Increasing climatic and anthropogenic pressure on the Matopiba agricultural frontier and its impacts on water resources: a case study of the Corrente River Basin L. Ferraz et al. https://doi.org/10.1007/s00704-026-06226-7
72. Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model C. Gao et al. https://doi.org/10.1016/j.jhydrol.2020.125337
73. Frontiers in Hydrology and Water Resources Research T. TANAKA et al. https://doi.org/10.3178/jjshwr.31.509
74. Spatial and temporal drought projections of northwestern Turkey S. Yeşilköy & L. Şaylan https://doi.org/10.1007/s00704-022-04029-0
75. Comparison of Bias Correction Methods for Summertime Daily Rainfall in South Korea Using Quantile Mapping and Machine Learning Model G. Seo & J. Ahn https://doi.org/10.3390/atmos14071057
76. Implications of CMIP6 Models‐Based Climate Biases and Runoff Sensitivity on Runoff Projection Uncertainties Over Central India S. Tyagi et al. https://doi.org/10.1002/joc.8661
77. Analysis of Anomalies Due to the ENSO and Long-Term Changes in Extreme Precipitation Indices Using Data from Ground Stations L. Vargas-León & J. Giraldo-Osorio https://doi.org/10.3390/hydrology11010007
78. Evaluating the added value of the new Swiss climate scenarios for hydrology: An example from the Thur catchment O. Rössler et al. https://doi.org/10.1016/j.cliser.2019.01.001
79. Combining Hydrological Modeling and Regional Climate Projections to Assess the Climate Change Impact on the Water Resources of Dam Reservoirs M. Savino et al. https://doi.org/10.3390/w15244243
80. Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain N. Reyniers et al. https://doi.org/10.5194/essd-17-2113-2025
81. Hydrology and Carbon Flux Interconnections in a Hemiboreal Forest: Impacts of Heatwaves in Järvselja, Estonia F. Civitate et al. https://doi.org/10.3390/f17030297
82. Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China S. Pan et al. https://doi.org/10.3390/atmos10080453
83. Assessment of meteorological drought change in the 21st century based on CMIP6 multi-model ensemble projections over mainland China Z. Song et al. https://doi.org/10.1016/j.jhydrol.2021.126643
84. Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool A. Casanueva et al. https://doi.org/10.1007/s10584-018-2167-5
85. Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin, Korea M. Lee et al. https://doi.org/10.1002/hyp.13469
86. Meteorological trends over Satluj River Basin in Indian Himalaya under climate change scenarios A. Gupta et al. https://doi.org/10.1007/s12040-020-01424-x
87. 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
88. Hydrologic modeling in mountainous terrains with monsoonal climate: Do gridded precipitation data improve streamflow simulations? P. de la Fraga et al. https://doi.org/10.3389/frwa.2026.1788195
89. Comparison of hydrological regime of glacierized Marshyangdi and Tamor river basins of Nepal A. Gupta et al. https://doi.org/10.1007/s12665-019-8443-5
90. Appraisal of Climate Change and Its Impact on Water Resources of Pakistan: A Case Study of Mangla Watershed H. Haider et al. https://doi.org/10.3390/atmos11101071
91. Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of Precipitation D. Gampe et al. https://doi.org/10.1175/JHM-D-18-0108.1
92. Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas M. Rogelis & M. Werner https://doi.org/10.5194/hess-22-853-2018
93. Added value of dynamical downscaling for hydrological projections in the Chungju Basin, Korea M. Lee et al. https://doi.org/10.1002/joc.5825
94. 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
95. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? A. Cannon et al. https://doi.org/10.1175/JCLI-D-14-00754.1
96. Effect of bias correction on CORDEX simulations of hydrological droughts in the Western Cape region of South Africa M. Naik & B. Abiodun https://doi.org/10.1007/s00704-025-05951-9
97. CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations A. Mezghani et al. https://doi.org/10.5194/essd-9-905-2017
98. Including rainfall sequencing and autocorrelation adjustment as a component of bias correction for hydroclimate projections N. Potter et al. https://doi.org/10.1016/j.envsoft.2026.107121
99. CONSIDERATIONS ON THE USE OF QUANTILE MAPPING BIAS CORRECTION FOR THE IMPACT ASSESSMENT OF CLIMATE CHANGE S. WATANABE et al. https://doi.org/10.2208/jscejhe.73.I_121
100. Significant contribution of bias correction methods to uncertainty in future runoff projections under CMIP6 climate change S. Chae & E. Chung https://doi.org/10.1016/j.ejrh.2024.101973
101. Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change J. Heo et al. https://doi.org/10.3390/w11071475
102. Multi-site bias correction of climate model outputs for hydro-meteorological impact studies: An application over a watershed in China T. Su et al. https://doi.org/10.1002/hyp.13750
103. Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections N. Potter et al. https://doi.org/10.5194/hess-24-2963-2020
104. 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
105. The critical role of uncertainty in projections of hydrological extremes H. Meresa & R. Romanowicz https://doi.org/10.5194/hess-21-4245-2017
106. Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia J. Evans et al. https://doi.org/10.5194/hess-21-409-2017
107. Bias Correction of Climate Model’s Precipitation Using the Copula Method and Its Application in River Basin Simulation G. Lazoglou et al. https://doi.org/10.3390/w11030600
108. Bias Correction and Evaluation of Precipitation Data from the CORDEX Regional Climate Model for Monitoring Climate Change in the Wadi Chemora Basin (Northeastern Algeria) S. Derdour et al. https://doi.org/10.3390/atmos13111876
109. Trends and Future Projections of Extreme Precipitation Indices in Limpopo Province, South Africa M. Mengistu et al. https://doi.org/10.3390/hydrology13050121
110. Projected changes in the near‐future mean climate and extreme climate events in northeast Thailand D. Khadka et al. https://doi.org/10.1002/joc.7377
111. An R package for assessment of statistical downscaling methods for hydrological climate change impact studies M. Hanel et al. https://doi.org/10.1016/j.envsoft.2017.03.036
112. Spatiotemporal fusion of reanalysis and in situ data for censored threshold exceedances of PM2.5 M. Cuba et al. https://doi.org/10.1214/26-AOAS2238
113. Influence of bias-correcting global climate models for regional climate simulations over the CORDEX-Australasia domain using WRF K. Wamahiu et al. https://doi.org/10.1007/s00704-020-03254-9
114. Correction of mesoscale model daily precipitation data over Northwestern Himalaya U. Devi et al. https://doi.org/10.1007/s00704-020-03409-8
115. Spatio-temporal variation of precipitation projection based on bias-adjusted CORDEX-SA regional climate model simulations for arid and semi-arid region A. Ghaemi et al. https://doi.org/10.3354/cr01722
116. Evaluating the uncertainty of climate model structure and bias correction on the hydrological impact of projected climate change in a Mediterranean catchment A. Senatore et al. https://doi.org/10.1016/j.ejrh.2022.101120
117. Using high-resolution climate change information in water management: a decision-makers' perspective H. Orr et al. https://doi.org/10.1098/rsta.2020.0219
118. 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
119. 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
120. Projected effects of climate change in precipitation and streamflows of the Upper Orinoco River Basin from global and regional climate models H. Salas et al. https://doi.org/10.2166/wcc.2024.659
121. Bias Correction for Precipitation Simulated by RegCM4 over the Upper Reaches of the Yangtze River Based on the Mixed Distribution Quantile Mapping Method B. Li et al. https://doi.org/10.3390/atmos12121566
122. Historical trends and high-resolution future climate projections in northern Tuscany (Italy) M. D'Oria et al. https://doi.org/10.1016/j.jhydrol.2017.10.054
123. High-end climate change impact on European runoff and low flows – exploring the effects of forcing biases L. Papadimitriou et al. https://doi.org/10.5194/hess-20-1785-2016
124. Evaluation of Three Error-Correction Models Based on the Matched Pixel Scale Ground “Truth”: A Case Study of MCD43A3 V006 Over the Heihe River Basin, China J. Wang et al. https://doi.org/10.1109/JSTARS.2022.3213184
125. Quantification of uncertainties in streamflow extremes in the Chaliyar river basin, India under climate change S. Ansa Thasneem et al. https://doi.org/10.1007/s00704-023-04410-7
126. Simulating event-scale rainfall erosivity across European climatic regions F. Matthews et al. https://doi.org/10.1016/j.catena.2022.106157
127. Comparison of projected rice blast epidemics in the Korean Peninsula between the CMIP5 and CMIP6 scenarios K. Lee et al. https://doi.org/10.1007/s10584-022-03410-2
128. Bias correction of extreme precipitation using the Meta-statistical Extreme Value framework across Japan K. Sakikawa et al. https://doi.org/10.3178/hrl.26-00004
129. Assessing Spatio-Temporal Hydrological Impacts of Climate Change in the Siliana Watershed, Northwestern Tunisia I. El Ghoul et al. https://doi.org/10.3390/atmos15101209
130. Hydro-Meteorological Drought Projections into the 21-st Century for Selected Polish Catchments H. Meresa et al. https://doi.org/10.3390/w8050206
131. Streamflow variability over the 1881–2011 period in northern Québec: comparison of hydrological reconstructions based on tree rings and geopotential height field reanalysis P. Brigode et al. https://doi.org/10.5194/cp-12-1785-2016
132. Multi-Model Ensemble Machine Learning Approaches to Project Climatic Scenarios in a River Basin in the Pyrenees N. Bilbao-Barrenetxea et al. https://doi.org/10.1007/s41748-024-00408-x
133. Characterization of hydro-meteorological droughts based on dynamic future scenarios and effective rainfall over Central Malawi S. Chikabvumbwa et al. https://doi.org/10.1007/s00704-023-04715-7
134. Assessment of characteristic changes of regional estimation of extreme rainfall under climate change: A case study in a tropical monsoon region with the climate projections from CMIP6 model S. Das et al. https://doi.org/10.1016/j.jhydrol.2022.128002
135. Characteristics of rainfall events in regional climate model simulations for the Czech Republic V. Svoboda et al. https://doi.org/10.5194/hess-21-963-2017
136. Effects of Bias-Correcting Climate Model Data on the Projection of Future Changes in High Flows V. Wörner et al. https://doi.org/10.3390/hydrology6020046