198 citations as recorded by crossref.

1. Global impact of climate change on soil erosion and potential for adaptation through soil conservation J. Eekhout & J. de Vente https://doi.org/10.1016/j.earscirev.2022.103921
2. Frequency-intensity-distribution bias correction and trend analysis of high-resolution CMIP6 precipitation data over a tropical river basin D. Jose & G. Dwarakish https://doi.org/10.1007/s00704-022-04078-5
3. The assessment of change in human heat stress risk in Brazil projected by the CMIP6 models L. Gohar et al. https://doi.org/10.1088/2515-7620/ad92f7
4. 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
5. Convection-permitting regional climate simulations for representing floods in small- and medium-sized catchments in the Eastern Alps C. Reszler et al. https://doi.org/10.5194/nhess-18-2653-2018
6. Towards cooperation on transboundary rivers: achieving a win–win balance between upstream hydropower generation and downstream water demand under climate change T. Abraham et al. https://doi.org/10.1016/j.jhydrol.2026.135111
7. Uncertainty quantification in intensity-duration-frequency curves under climate change: Implications for flood-prone tropical cities W. Zhao et al. https://doi.org/10.1016/j.atmosres.2022.106070
8. Evaluation of change factor methods in downscaling extreme precipitation over India M. VishnuPriya & V. Agilan https://doi.org/10.1016/j.jhydrol.2022.128531
9. 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
10. On the role of wave climate temporal variability in bias correction of GCM-RCM wave simulations A. Lira Loarca et al. https://doi.org/10.1007/s00382-023-06756-0
11. Analysis of past and projected changes in extreme precipitation indices in some watersheds in Côte d'Ivoire N. Amichiatchi et al. https://doi.org/10.2166/wcc.2023.365
12. Development of intensity–duration–frequency relationships in Khon Kaen City, Thailand under changing climate using GCMs and a simple scaling method K. Tedprasith & W. Lohpaisankrit https://doi.org/10.2166/wcc.2024.533
13. Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling A. Reder et al. https://doi.org/10.1038/s41598-024-84527-5
14. A new method of reducing dynamic downscaling precipitation bias in drylands: Wet-day Quantile Delta Mapping (WQDM) X. Shen et al. https://doi.org/10.1016/j.accre.2026.06.029
15. Assessing the Flood and Drought Regulation Capacity of Dams in a Changing Climate: An Application to the Largest Hydropower Dam in Africa T. Abraham et al. https://doi.org/10.1007/s41748-025-00815-8
16. Prediction of groundwater level fluctuations under climate change based on machine learning algorithms in the Mashhad aquifer, Iran G. Panahi et al. https://doi.org/10.2166/wcc.2023.027
17. Quantifying uncertainty and sensitivity in climate risk assessments: Varying hazard, exposure and vulnerability modelling choices L. Dawkins et al. https://doi.org/10.1016/j.crm.2023.100511
18. Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning S. Majnooni et al. https://doi.org/10.1080/02626667.2023.2248112
19. Variations in Present and Future Hourly Extreme Rainfall: Insights from High-Resolution Data and Novel Temporal Disaggregation Model Y. Dai et al. https://doi.org/10.3390/w16233463
20. Future flood and flood-on-drought peaks and volumes in the Northern Apennines through bias-correction and rainfall-runoff transformation of hourly climate forcings M. Neri et al. https://doi.org/10.1016/j.ejrh.2026.103864
21. How future changes in irrigation water supply and demand affect water security in a Mediterranean catchment J. Eekhout et al. https://doi.org/10.1016/j.agwat.2024.108818
22. Development of daily downscaled, bias-corrected CMIP6 climate datasets for estimating reference evapotranspiration (ETo) in South Asia A. Saha et al. https://doi.org/10.1038/s41597-025-06149-4
23. 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
24. Assessment of the Impacts of Climate Change Scenarios on Maize Yield and Irrigation Water Using the CropSyst Model: An Application in Northern Greece P. Koukouli et al. https://doi.org/10.3390/agronomy15030638
25. High-resolution machine-learning downscaled climate projections and extreme hazard assessment over monsoon-dominated Odisha, India N. Kar & S. Sahu https://doi.org/10.1080/19475705.2026.2710404
26. 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
27. Impact of downscaled rainfall biases on projected runoff changes S. Charles et al. https://doi.org/10.5194/hess-24-2981-2020
28. Climate change impact projections at the catchment scale in Tunisia using the multi-model ensemble mean approach S. Moussa et al. https://doi.org/10.1007/s12517-018-3532-4
29. A combined wavelet analysis-quantile mapping (WA-QM) method for bias correction: capturing the intra-annual temporal patterns in climate model precipitation simulations and projections X. Wu et al. https://doi.org/10.1088/1748-9326/adae23
30. 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
31. Warming climate and water withdrawals threaten river flow connectivity in China J. Gou et al. https://doi.org/10.1073/pnas.2421046122
32. Bias correction of precipitation from convection-permitting models at the point scale: a case study in Switzerland T. Nguyen et al. https://doi.org/10.1007/s10584-025-04079-z
33. Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework M. Rahman et al. https://doi.org/10.1016/j.asr.2024.03.033
34. Downscaling fire weather extremes from historical and projected climate models P. Jain et al. https://doi.org/10.1007/s10584-020-02865-5
35. 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
36. Future overheating risk in prisons in England and Wales using the UK climate projections M. Sanderson et al. https://doi.org/10.3389/fclim.2025.1611715
37. Climate change reshapes bioclimatic environments in China’s dry–wet transition zones M. Li et al. https://doi.org/10.1016/j.jhydrol.2024.131122
38. High-quality reconstruction of China’s natural streamflow C. Miao et al. https://doi.org/10.1016/j.scib.2021.09.022
39. Climate–yield interactions in West Africa: Machine learning insights for cocoa production in Ghana and Côte d’Ivoire S. Obahoundje et al. https://doi.org/10.1371/journal.pclm.0000970
40. A three-layer parameterization framework for ENSO-runoff coupling: Enhanced flood risk in the Huaihe River Basin under climate change X. Meng et al. https://doi.org/10.1016/j.ejrh.2026.103562
41. Probable maximum precipitation (PMP) and flood (PMF) risk charts in Hodna basin, Algeria S. Zeroual et al. https://doi.org/10.1007/s00703-022-00879-5
42. ECHIDNA: Extreme Climate Historical and Future Indices Data under Numerous Approaches across Major Chinese River Basins Based on CMIP6 Multi-Model Ensemble L. Xinlong et al. https://doi.org/10.1038/s41597-026-07510-x
43. Identifying climate extremes in Southern Africa through advanced bias correction of climate projections L. Trentini et al. https://doi.org/10.1007/s42865-025-00097-y
44. Future wind energy potential for wind farm development in Africa based on bias-corrected NEX-GDDP-CMIP6 wind projections A. Ogunrinde et al. https://doi.org/10.1016/j.clet.2026.101145
45. 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
46. Climate adjusted projections of the distribution and frequency of poor air quality days for the contiguous United States B. Wilson et al. https://doi.org/10.3389/feart.2024.1320170
47. Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods W. Qian & H. Chang https://doi.org/10.3390/ijerph18041992
48. Review of global climate models used for drought assessment and forecasting S. Tayyaba et al. https://doi.org/10.1007/s43832-026-00352-z
49. 21st Century alpine climate change S. Kotlarski et al. https://doi.org/10.1007/s00382-022-06303-3
50. Scenario set-up and the new CMIP6-based climate-related forcings provided within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3b, group I and II) K. Frieler et al. https://doi.org/10.5194/gmd-19-4095-2026
51. 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
52. Effect of climate change on surface water availability at hare watershed, rift valley basin, South Ethiopia C. Asnik et al. https://doi.org/10.1016/j.htopen.2026.100073
53. Analysis of extreme precipitation and its variability under climate change in a river basin S. Ansa Thasneem et al. https://doi.org/10.1007/s11069-019-03664-7
54. Flood Risk Assessment at the Administrative District Level Based on SSP Scenarios B. Cho et al. https://doi.org/10.9798/KOSHAM.2026.26.2.23
55. CMIP6 multi-model ensemble projections of Boro rice irrigation requirements and scheduling under climate change in North-Western Bangladesh M. Islam et al. https://doi.org/10.1016/j.rineng.2026.111968
56. Spring frost risk for regional apple production under a warmer climate C. Unterberger et al. https://doi.org/10.1371/journal.pone.0200201
57. Research on quantitative assessment of climate change risk at an urban scale: Review of recent progress and outlook of future direction B. Ye et al. https://doi.org/10.1016/j.rser.2020.110415
58. Multivariate adjustment of drizzle bias using machine learning in European climate projections G. Lazoglou et al. https://doi.org/10.5194/gmd-17-4689-2024
59. Multi-model ensembles for regional and national wheat yield forecasts in Argentina M. Zachow et al. https://doi.org/10.1088/1748-9326/ad627c
60. 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
61. Projected heat wave increasing trends over China based on combined dynamical and multiple statistical downscaling methods M. Zhang et al. https://doi.org/10.1016/j.accre.2023.09.001
62. 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
63. Regional Climate Model Biases, Their Dependence on Synoptic Circulation Biases and the Potential for Bias Adjustment: A Process‐Oriented Evaluation of the Austrian Regional Climate Projections D. Maraun et al. https://doi.org/10.1029/2020JD032824
64. Evaluation of a multi-staged bias correction approach on CHIRP and CHIRPS rainfall product: a case study of the Lake Hawassa watershed T. Beyene et al. https://doi.org/10.2166/wcc.2023.457
65. Past and future changes of the Austrian climate – Importance for tourism M. Olefs et al. https://doi.org/10.1016/j.jort.2021.100395
66. Remaining error sources in bias-corrected climate model outputs J. Chen et al. https://doi.org/10.1007/s10584-020-02744-z
67. 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
68. The implications of bias correction methods and climate model ensembles on soil erosion projections under climate change J. Eekhout & J. de Vente https://doi.org/10.1002/esp.4563
69. Risk assessment in water resources planning under climate change at the Júcar River basin S. Suárez-Almiñana et al. https://doi.org/10.5194/hess-24-5297-2020
70. The current and future warming of Lake Titicaca D. Dinh et al. https://doi.org/10.1016/j.jglr.2026.102752
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. The Future of Atmospheric Heatwaves in Chile Projected by a Regional Climate Model J. Petit‐Laurent et al. https://doi.org/10.1002/joc.70567
73. Future climate risk to UK agriculture from compound events F. Garry et al. https://doi.org/10.1016/j.crm.2021.100282
74. Novel application of a process convolution approach for calibrating output from numerical models M. Holthuijzen et al. https://doi.org/10.1002/env.2822
75. Investigating future climate change impacts on streamflow and drought characterization in the Arno River basin, Tuscany, Italy, using CMIP6 and SWAT+ modelling H. Talpur et al. https://doi.org/10.1016/j.watcyc.2026.03.006
76. SIMPCCe: una herramienta para el análisis de aportaciones a embalses ante escenarios de cambio climático S. Navas & M. Del Jesus https://doi.org/10.4995/ia.2025.23217
77. Modelling the impact of climate change on soil erosion and sediment yield: a case study in a sub-tropical catchment, India J. Rajbanshi & S. Bhattacharya https://doi.org/10.1007/s40808-021-01117-4
78. Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al. https://doi.org/10.1016/j.jhydrol.2025.133320
79. Assessment of Nonstationarity and Uncertainty in Precipitation Extremes of a River Basin Under Climate Change S. Ansa Thasneem et al. https://doi.org/10.1007/s10666-021-09752-y
80. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. https://doi.org/10.2166/wcc.2023.230
81. Linking local climate scenarios to global warming levels: applicability, prospects and uncertainties B. Becsi & H. Formayer https://doi.org/10.1088/2752-5295/ad574e
82. Glacio-hydrological melt and run-off modelling: application of a limits of acceptability framework for model comparison and selection J. Mackay et al. https://doi.org/10.5194/tc-12-2175-2018
83. 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
84. Evaluation of bias correction techniques for generating high-resolution daily temperature projections from CMIP6 models X. Li & Z. Li https://doi.org/10.1007/s00382-023-06778-8
85. Projected Changes in Southeast Asian Sea Surface Characteristics Using CMIP6 GCMs O. Salehie et al. https://doi.org/10.1007/s41748-024-00480-3
86. The uneven impact of climate change on drought with elevation in the Canary Islands J. Carrillo et al. https://doi.org/10.1038/s41612-023-00358-7
87. Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal? P. Reiter & M. Casper https://doi.org/10.3390/hydrology11090143
88. Warming may offset impact of precipitation changes on riverine nitrogen loading G. Zhao et al. https://doi.org/10.1073/pnas.2220616120
89. A process‐based soil erosion model ensemble to assess model uncertainty in climate‐change impact assessments J. Eekhout et al. https://doi.org/10.1002/ldr.3920
90. Spatiotemporal analysis of CMIP6-based climate extremes and their implications for sustainable watershed management in the Gidabo watershed, Ethiopia D. Dogiso et al. https://doi.org/10.1038/s41598-025-02408-x
91. Analysis of Climate Change Projections for Mozambique under the Representative Concentration Pathways A. Mavume et al. https://doi.org/10.3390/atmos12050588
92. Upper Danube low flow in the Anthropocene: Simulating impacts and changes from 1870 to 2100 P. Stanzel et al. https://doi.org/10.1016/j.ejrh.2026.103862
93. High-resolution climate datasets in hydrological impact studies: Assessing their value in alpine and pre-alpine catchments in southeastern Austria S. Peßenteiner et al. https://doi.org/10.1016/j.ejrh.2021.100962
94. Changes of hydro-meteorological trigger conditions for debris flows in a future alpine climate R. Kaitna et al. https://doi.org/10.1016/j.scitotenv.2023.162227
95. Downscaled climate model erosivity projections and drivers of change across distinct climate regions N. Bulovic et al. https://doi.org/10.1016/j.catena.2024.108250
96. Operational low-flow forecasting using LSTMs J. Deng et al. https://doi.org/10.3389/frwa.2023.1332678
97. WITHDRAWN: Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. https://doi.org/10.1016/j.hydroa.2021.100109
98. Future Population Exposure to Extreme Precipitation Slows Down in China’s Urban Agglomerations Despite Global Warming L. Tang et al. https://doi.org/10.1038/s42949-025-00285-x
99. Assessing the hillslope-channel contributions to the catchment sediment balance under climate change J. Eekhout et al. https://doi.org/10.1016/j.envsoft.2023.105890
100. The effect of univariate bias adjustment on multivariate hazard estimates J. Zscheischler et al. https://doi.org/10.5194/esd-10-31-2019
101. A framework to compare the performance of spatial disaggregation methods of statistical climate downscaling in South America C. Aboitiz et al. https://doi.org/10.1016/j.ejrh.2026.103834
102. Projected climate regime over Pakistan and its implications for hydrology in the Hunza River Basin using CMIP6 GCMs M. Hassan et al. https://doi.org/10.1007/s00382-025-07768-8
103. Risk of extreme climate impacts on European Norway spruce forest: drought and frost in the climate emergency D. Ray et al. https://doi.org/10.1007/s10584-025-03954-z
104. Co-producing an urban heat climate service for UK cities: A case study of Belfast, Northern Ireland V. Ramsey et al. https://doi.org/10.1016/j.cliser.2024.100464
105. 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
106. Near-real-time forecasting of reservoir inflows using explainable machine learning and short-term weather forecasts K. Sushanth et al. https://doi.org/10.1007/s00477-023-02489-y
107. Climate change impacts on Robusta coffee production in Vietnam L. Anh Dinh et al. https://doi.org/10.1088/1748-9326/ae14d1
108. Improving Daily CMIP6 Precipitation in Southern Africa Through Bias Correction—Part 1: Spatiotemporal Characteristics A. Addisuu et al. https://doi.org/10.3390/cli13050095
109. Assessing climate risk using ensembles: A novel framework for applying and extending open-source climate risk assessment platforms L. Dawkins et al. https://doi.org/10.1016/j.crm.2023.100510
110. Multi-region lifetime assessment of reinforced concrete structures subjected to carbonation and climate change E. Bastidas-Arteaga et al. https://doi.org/10.1016/j.istruc.2022.09.061
111. Simulating Central-European forests in the 21st century. Effects of climate change, disturbances and management on stocks, productivity and harvests M. Lexer et al. https://doi.org/10.1016/j.jenvman.2026.129713
112. 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
113. 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
114. Exploring the effects of climate change on the water balance of a continuously moving deep-seated landslide T. Zieher et al. https://doi.org/10.1007/s11069-022-05558-7
115. Multi-scenario analysis of wheat irrigation requirements under climate change: assessment of CMIP6 models for crop water modeling in Bangladesh M. Islam et al. https://doi.org/10.2166/wcc.2026.405
116. A Bias-Corrected HighResMIP Dataset for Impact Assessment Studies F. Yakubu et al. https://doi.org/10.1038/s41597-026-07709-y
117. Future Climate Projections for Tacna, Peru: Assessing Changes in Temperature and Precipitation G. De la Cruz et al. https://doi.org/10.3390/atmos16020144
118. Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch A. Casanueva et al. https://doi.org/10.1002/asl.978
119. 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
120. Future changes to high impact weather in the UK H. Hanlon et al. https://doi.org/10.1007/s10584-021-03100-5
121. pyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies A. Hernanz et al. https://doi.org/10.1016/j.cliser.2023.100408
122. The Construction of Probabilistic Wildfire Risk Estimates for Individual Real Estate Parcels for the Contiguous United States E. Kearns et al. https://doi.org/10.3390/fire5040117
123. An improved empirical quantile mapping approach for bias correction of extreme values in climate model simulations K. Byun & A. Hamlet https://doi.org/10.1088/1748-9326/ad9b3d
124. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0) S. Lange https://doi.org/10.5194/gmd-12-3055-2019
125. Sensitivity of Glacier Runoff to Winter Snow Thickness Investigated for Vatnajökull Ice Cap, Iceland, Using Numerical Models and Observations L. Schmidt et al. https://doi.org/10.3390/atmos9110450
126. Bias Correction for Climate Model Output (1) Method Characteristics and Classification S. WATANABE https://doi.org/10.3178/jjshwr.33.243
127. How soil erosion model conceptualization affects soil loss projections under climate change J. Eekhout & J. De Vente https://doi.org/10.1177/0309133319871937
128. 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
129. Growth Response of Red Oaks to Climatic Conditions in the Lower Mississippi Alluvial Valley: Implications for Bottomland Hardwood Restoration with a Changing Climate J. Choi et al. https://doi.org/10.3390/cli11010010
130. Robust handling of extremes in quantile mapping – “Murder your darlings” P. Berg et al. https://doi.org/10.5194/gmd-17-8173-2024
131. Real-time streamflow forecasting in a reservoir-regulated river basin using explainable machine learning and conceptual reservoir module K. Sushanth et al. https://doi.org/10.1016/j.scitotenv.2022.160680
132. Coupling of hydrological drought and flood extremes under climate change in the Kamp catchment, Austria Z. Babker et al. https://doi.org/10.1016/j.ejrh.2026.103943
133. A framework for the predictive mapping of forest soil properties in mountain areas A. Simon et al. https://doi.org/10.1016/j.geoderma.2020.114383
134. 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
135. Global warming threatens conservation status of alpine EU habitat types in the European Eastern Alps P. Schwager & C. Berg https://doi.org/10.1007/s10113-019-01554-z
136. Evaluation of CMIP6 models for projecting climate trends and variability in the Didessa sub-basin, Ethiopia: Implications for the Blue Nile basin S. Degefu et al. https://doi.org/10.1016/j.ejrh.2026.104072
137. Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach M. Olefs et al. https://doi.org/10.3390/atmos11121330
138. Improving the Regional Precipitation Simulation Corrected by Satellite Observation Using Quantile Mapping S. Liu et al. https://doi.org/10.3390/rs17101716
139. Seasonality and Impact Factor Analysis of Streamflow Sensitivity to Climate Change Across China J. Gou et al. https://doi.org/10.1029/2022EF003062
140. Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires https://doi.org/10.1007/s10584-023-03597-y
141. 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
142. Near-term regional climate change over Bangladesh Y. Choi et al. https://doi.org/10.1007/s00382-021-05856-z
143. Atmospheric River Sequences as Indicators of Hydrologic Hazard in Historical Reanalysis and GFDL SPEAR Future Climate Projections C. Bowers et al. https://doi.org/10.1029/2023EF003536
144. Land use and climate change-induced soil erosion mapping in a sub-tropical environment S. Pal et al. https://doi.org/10.1080/19475705.2023.2270129
145. Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand S. Wuthiwongtyohtin https://doi.org/10.2166/wcc.2020.021
146. Spatiotemporal dynamics of landslide susceptibility under future climate change and land use scenarios K. Ullah et al. https://doi.org/10.1088/1748-9326/ad8a72
147. MIdASv0.2.1 – MultI-scale bias AdjuStment P. Berg et al. https://doi.org/10.5194/gmd-15-6165-2022
148. Non-Stationary Precipitation Frequency Estimates for Resilient Infrastructure Design in a Changing Climate: A Case Study in Sydney S. Doulabian et al. https://doi.org/10.3390/hydrology10060117
149. Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling J. Van de Velde et al. https://doi.org/10.3390/w13111573
150. 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
151. Compilation of a guideline providing comprehensive information on freely available climate change data and facilitating their efficient retrieval B. Chimani et al. https://doi.org/10.1016/j.cliser.2020.100179
152. 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
153. Climate projections for glacier change modelling over the Himalayas M. Jury et al. https://doi.org/10.1002/joc.6298
154. Klimaszenarien zur Optimierung eines Talsperren-Betriebsplans H. Lohr et al. https://doi.org/10.1007/s35147-023-1843-1
155. Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin D. Jose & G. Dwarakish https://doi.org/10.1007/s13143-021-00240-7
156. Adaptation of root zone storage capacity to climate change and its effects on future streamflow in Alpine catchments: towards non-stationary model parameters M. Ponds et al. https://doi.org/10.5194/hess-29-3545-2025
157. Advancements and challenges in bias correction quantile mapping for climate projections: A comprehensive review S. Halim https://doi.org/10.23939/mmc2025.03.841
158. Coupled weather-biosphere model (WRF-VPRM) response to extreme land cover scenarios over Ganga basin: a cross-component analysis V. Huggannavar & J. Indu https://doi.org/10.1088/2515-7620/acea1e
159. On the Management of Nature-Based Solutions in Open-Air Laboratories: New Insights and Future Perspectives G. Gallotti et al. https://doi.org/10.3390/resources10040036
160. A simple hybrid statistical–dynamical downscaling method for emulating regional climate models over Western Europe. Evaluation, application, and role of added value? J. Boé et al. https://doi.org/10.1007/s00382-022-06552-2
161. Increased sediment deposition triggered by climate change impacts freshwater pearl mussel habitats and metapopulations D. Baldan et al. https://doi.org/10.1111/1365-2664.13940
162. Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. https://doi.org/10.1016/j.jhydrol.2021.127261
163. Apparent contradiction in the projected climatic water balance for Austria: wetter conditions on average versus higher probability of meteorological droughts K. Haslinger et al. https://doi.org/10.5194/nhess-23-2749-2023
164. Bioclimatic indicators dataset for the orographically complex Canary Islands archipelago P. Sosa-Guillén et al. https://doi.org/10.1038/s41597-024-04134-x
165. Comparative analysis of bias correction methods for projecting extreme precipitation and temeprature indices in Pakistan Z. Ali et al. https://doi.org/10.1016/j.atmosres.2025.107957
166. 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
167. 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
168. 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
169. Functional redundant soil fauna and microbial groups and processes were fairly resistant to drought in an agroecosystem A. Watzinger et al. https://doi.org/10.1007/s00374-023-01728-2
170. A framework for projecting future intensity-duration-frequency (IDF) curves based on CORDEX Southeast Asia multi-model simulations: An application for two cities in Southern Vietnam W. Zhao et al. https://doi.org/10.1016/j.jhydrol.2021.126461
171. Seasonal climate models for national wheat yield forecasts in Brazil M. Zachow et al. https://doi.org/10.1016/j.agrformet.2023.109753
172. Meso-scale spatiotemporal analysis of future drought characteristics in Northern Thailand under climate change and variability K. Leerach et al. https://doi.org/10.1007/s00704-025-05662-1
173. Comparison of Multiple Surface Ocean Wind Products with Buoy Data over Blue Amazon (Brazilian Continental Margin) V. Paiva et al. https://doi.org/10.1155/2021/6680626
174. Impact Analysis of Univariate and Multivariate Bias Correction on Rice Irrigation Water Needs in Jiangxi Province, China W. Hanggoro et al. https://doi.org/10.3390/w12020381
175. On the importance of the reference data: Uncertainty partitioning of bias-adjusted climate simulations over eastern Canada J. Lavoie et al. https://doi.org/10.1016/j.cliser.2025.100619
176. 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
177. 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
178. Evaluating statistical downscaling and bias-correction methods for climate extremes across major Chinese River basins L. Xinlong et al. https://doi.org/10.1016/j.asej.2026.104186
179. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset S. Lange https://doi.org/10.5194/esd-9-627-2018
180. Assessing the Effect of Bias Correction Methods on the Development of Intensity–Duration–Frequency Curves Based on Projections from the CORDEX Central America GCM-RCM Multimodel-Ensemble M. Mendez et al. https://doi.org/10.3390/w16233473
181. A Three-Stage Calibration Pipeline for IMERG V07 Targeting Extreme-Intensity Bias: Application to Rainfall Erosivity Estimation over the Volga Region (2001–2024) A. Gafurov https://doi.org/10.3390/hydrology13060151
182. Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria S. Thaler et al. https://doi.org/10.3390/agriculture11111029
183. Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin W. Gumindoga et al. https://doi.org/10.5194/hess-23-2915-2019
184. The Time of Emergence (ToE) of the Andean Mediterranean sclerophyllous forest of Quillaja saponaria (Mol.) and Lithraea caustica (mol.) Hox. & Arn D. Martínez et al. https://doi.org/10.1016/j.foreco.2023.121169
185. Real-time reservoir operation using inflow and irrigation demand forecasts in a reservoir-regulated river basin K. Sushanth et al. https://doi.org/10.1016/j.scitotenv.2023.166806
186. Impact of solar geoengineering on temperatures over the Indonesian Maritime Continent H. Kuswanto et al. https://doi.org/10.1002/joc.7391
187. Towards process-informed bias correction of climate change simulations D. Maraun et al. https://doi.org/10.1038/nclimate3418
188. Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction M. Ivanov et al. https://doi.org/10.1175/JCLI-D-17-0765.1
189. Climate change impact on effective water use for hydropower generation of the Grand Ethiopian Renaissance Dam T. Abraham & Y. Liu https://doi.org/10.1007/s00704-025-05538-4
190. Bridging climate science and adaptation: A perspective on the construction of national climate scenarios K. van der Wiel et al. https://doi.org/10.1016/j.jemets.2026.100052
191. A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation Y. Shin et al. https://doi.org/10.3390/atmos11080775
192. CLIMBra - Climate Change Dataset for Brazil A. Ballarin et al. https://doi.org/10.1038/s41597-023-01956-z
193. Quantifying overheating risk in English schools: A spatially coherent climate risk assessment L. Dawkins et al. https://doi.org/10.1016/j.crm.2024.100602
194. Responses of Extreme Discharge to Changes in Surface-Air and Dewpoint Temperatures in Utah: Seasonality and Mechanisms T. Wright et al. https://doi.org/10.3390/w15040688
195. Future changes in annual, seasonal and monthly runoff signatures in contrasting Alpine catchments in Austria S. Hanus et al. https://doi.org/10.5194/hess-25-3429-2021
196. A simplified procedure to assess the effects of climate change on landslide hazard in a small area of the Southern Apennines in Italy G. Rianna et al. https://doi.org/10.1007/s11069-022-05656-6
197. A new method for runoff prediction error correction based on LS-SVM and a 4D copula joint distribution Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2021.126223
198. MPCID, A new high-resolution multi-precipitation concentration indicators dataset for mainland China D. Zhang et al. https://doi.org/10.1038/s41597-025-06515-2