1006 citations as recorded by crossref.

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27. Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador L. Campozano et al. https://doi.org/10.1155/2016/6526341
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32. 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
33. Climate futures for Western Nepal based on regional climate models in the CORDEX‐SA S. Dhaubanjar et al. https://doi.org/10.1002/joc.6327
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41. 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
42. Recent Climatic Mass Balance of the Schiaparelli Glacier at the Monte Sarmiento Massif and Reconstruction of Little Ice Age Climate by Simulating Steady-State Glacier Conditions S. Weidemann et al. https://doi.org/10.3390/geosciences10070272
43. Statistical downscaling for daily precipitation in Korea using combined PRISM, RCM, and quantile mapping: Part 1, methodology and evaluation in historical simulation M. Kim et al. https://doi.org/10.1007/s13143-016-0010-3
44. Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models K. Belitz & P. Stackelberg https://doi.org/10.1016/j.envsoft.2021.105006
45. Bias correction, historical evaluations, and future projections of climate simulations in the Wei River Basin using CORDEX-EA Y. Wang et al. https://doi.org/10.1007/s00704-022-04157-7
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47. 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
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50. Setting an environmental flow regime under climate change in a data-limited Mediterranean basin with temporary river M. Leone et al. https://doi.org/10.1016/j.ejrh.2024.101698
51. Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change A. Fonseca et al. https://doi.org/10.3390/su16083477
52. Unlocking the potential of pollarded oaks: a 375-year hydroclimate reconstruction from northcentral Spain A. Sanmiguel-Vallelado et al. https://doi.org/10.5194/bg-23-4287-2026
53. Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation S. Hwang & W. Graham https://doi.org/10.5194/hess-17-4481-2013
54. A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts Z. Huang et al. https://doi.org/10.1016/j.jhydrol.2022.127896
55. Evaluating methods for reconstructing large gaps in historic snow depth time series J. Aschauer & C. Marty https://doi.org/10.5194/gi-10-297-2021
56. Climate change impacts on multi-objective reservoir management: case study on the Seine River basin, France D. Dorchies et al. https://doi.org/10.1080/15715124.2013.865636
57. 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
58. Statistical reconstruction of daily precipitation and temperature fields in Switzerland back to 1864 L. Pfister et al. https://doi.org/10.5194/cp-16-663-2020
59. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. https://doi.org/10.2166/wcc.2023.230
60. 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
61. Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia A. Chinasho et al. https://doi.org/10.1155/2021/9657460
62. Climate change impacts on future snow, ice and rain runoff in a Swiss mountain catchment using multi-dataset calibration S. Etter et al. https://doi.org/10.1016/j.ejrh.2017.08.005
63. Diffusion-Model-Based Downscaling of Observed Sea Surface Height over the Kuroshio Extension Since 2000 Q. Han et al. https://doi.org/10.3390/atmos16050570
64. Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs M. Kamruzzaman et al. https://doi.org/10.1016/j.heliyon.2023.e16274
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66. Climate Change and Maize Yield in Iowa H. Xu et al. https://doi.org/10.1371/journal.pone.0156083
67. SnowQM 1.0: a fast R package for bias-correcting spatial fields of snow water equivalent using quantile mapping A. Michel et al. https://doi.org/10.5194/gmd-17-8969-2024
68. Changing compound rainfall events in Tasmania N. Earl et al. https://doi.org/10.1002/joc.7791
69. Uncertainty of annual runoff projections in Lithuanian rivers under a future climate V. Akstinas et al. https://doi.org/10.2166/nh.2019.004
70. Investigating seasonal drought severity-area-frequency (SAF) curve over Indian region: incorporating GCM and scenario uncertainties S. Das et al. https://doi.org/10.1007/s00477-021-02073-2
71. An extremes-weighted empirical quantile mapping for global climate model data bias correction for improved emphasis on extremes A. Rohith & R. Cibin https://doi.org/10.1007/s00704-024-04965-z
72. Multi-hazard could exacerbate in coastal Bangladesh in the context of climate change M. Rahman et al. https://doi.org/10.1016/j.jclepro.2024.142289
73. 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
74. Climate-informed irrigation and nitrogen strategies for Boro rice sustainability: DSSAT-CERES modeling under CMIP6 projections M. Islam et al. https://doi.org/10.1016/j.agwat.2026.110642
75. Identification of the Optimum Rain Gauge Network Density for Hydrological Modelling Based on Radar Rainfall Analysis Y. Gyasi-Agyei https://doi.org/10.3390/w12071906
76. Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows J. Daraio https://doi.org/10.3390/w12082312
77. Evaluating the Credibility of Downscaling: Integrating Scale, Trend, Extreme, and Climate Event into a Diagnostic Framework F. Sun et al. https://doi.org/10.1175/JAMC-D-20-0078.1
78. Evaluating the performance of RegCM4 in studies on irrigated and rainfed cotton crops A. Shikha et al. https://doi.org/10.1007/s12040-021-01705-z
79. Increased high flows in Germany under 1.5 K, 2.0 K and 3.0 K warming based on a large regional climate model ensemble A. Chandrasekar et al. https://doi.org/10.1007/s10584-026-04165-w
80. Projected effects of climate change on shallow landslides, North Shore Mountains, Vancouver, Canada M. Jakob & T. Owen https://doi.org/10.1016/j.geomorph.2021.107921
81. 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
82. The European climate under a 2 °C global warming R. Vautard et al. https://doi.org/10.1088/1748-9326/9/3/034006
83. Evaluating the Impact of Climate Change on Water Quality and Quantity in an Urban Watershed Using an Ensemble Approach N. Alamdari et al. https://doi.org/10.1007/s12237-019-00649-4
84. Global near real-time 500 m 10 d FPAR dataset from MODIS and VIIRS for operational agricultural monitoring and crop yield forecasting L. Seguini et al. https://doi.org/10.5194/essd-18-309-2026
85. Bias correcting instantaneous peak flows generated using a continuous, semi‐distributed hydrologic model P. Spellman et al. https://doi.org/10.1111/jfr3.12342
86. Climate change threats to urban water supply in the Higueras river basin, Huánuco, Peru J. López et al. https://doi.org/10.2166/wpt.2026.465
87. Station-scale bias correction and uncertainty analysis for the estimation of irrigation water requirements in the Swiss Rhone catchment under climate change P. Smith et al. https://doi.org/10.1007/s10584-014-1263-4
88. Climate change and vine protection : the case of mildews management in Burgundy S. Zito et al. https://doi.org/10.1051/e3sconf/20185001006
89. Future rainfall projections for the Lower Mekong Basin using CMIP6 dynamical downscaling L. Trinh-Tuan et al. https://doi.org/10.2166/wcc.2025.793
90. Climate change impact assessment on the water resources of the Upper Yamuna River Basin in India R. Rathee & S. Mishra https://doi.org/10.1007/s10668-023-03398-4
91. 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
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116. High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin Z. Asong et al. https://doi.org/10.5194/essd-12-629-2020
117. Improved bioclimatic variables from regional climate models: a comparative analysis of three classic bias adjustment methods and a novel adjustment approach F. Bangelesa et al. https://doi.org/10.1007/s00704-026-06039-8
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119. Assessment of precipitation and its extreme precipitation changes over the Himalayan Bhilangana River Basin, India B. Joshi et al. https://doi.org/10.1007/s00704-025-05881-6
120. 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
121. New insights into the decadal variability in glacier volume of a tropical ice cap, Antisana (0°29′ S, 78°09′ W), explained by the morpho-topographic and climatic context R. Basantes-Serrano et al. https://doi.org/10.5194/tc-16-4659-2022
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125. Unveiling the future water pulse of central asia: a comprehensive 21st century hydrological forecast from stochastic water balance modeling T. Siegfried et al. https://doi.org/10.1007/s10584-024-03799-y
126. Assessment of Future Rainfall Quantile Changes in South Korea Based on a CMIP6 Multi-Model Ensemble S. Kim et al. https://doi.org/10.3390/w17060894
127. Seasonal drought prediction for semiarid northeast Brazil: what is the added value of a process-based hydrological model? T. Pilz et al. https://doi.org/10.5194/hess-23-1951-2019
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133. Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy L. Sangelantoni et al. https://doi.org/10.1007/s00704-018-2406-8
134. Performance of seasonal forecasts of Douro and Port wine production J. Santos et al. https://doi.org/10.1016/j.agrformet.2020.108095
135. Future energy demands and mix optimization under changing climate and socioeconomic scenarios A. Khourchid et al. https://doi.org/10.1016/j.nexus.2026.100757
136. Trigger thresholds and drivers of meteorological to agricultural drought propagation in the Hanjiang basin under climate change K. Yang et al. https://doi.org/10.2166/wcc.2026.233
137. Improving the statistical downscaling performance of climatic parameters with convolutional neural networks A. Hosseini Baghanam et al. https://doi.org/10.2166/wcc.2024.592
138. Future projections of Malaysia daily precipitation characteristics using bias correction technique S. Ngai et al. https://doi.org/10.1016/j.atmosres.2020.104926
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141. Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin X. Tang et al. https://doi.org/10.3390/rs13020312
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144. Integrating Structural Vulnerability Analysis and Data-Driven Machine Learning to Evaluate Storm Impacts on the Power Grid P. Watson et al. https://doi.org/10.1109/ACCESS.2024.3396414
145. Historical and projected changes in Extreme High Temperature events over East Africa and associated with meteorological conditions using CMIP6 models P. Das et al. https://doi.org/10.1016/j.gloplacha.2023.104068
146. Study on River Protection and Improvement Based on a Comprehensive Statistical Model in a Coastal Plain River Network J. Wang et al. https://doi.org/10.3390/su16093518
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148. A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics Q. Kong & M. Huber https://doi.org/10.1038/s41597-025-04527-6
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151. Glacier-influenced hydrological regimes in the Afghanistan Hindu Kush Himalaya under current and future climate J. Shokory et al. https://doi.org/10.2166/nh.2025.082
152. Regional-scale seasonal forecast of surface water availability in a semi-arid environment: The case of Ceará State in Northeast of Brazil E. Rottler et al. https://doi.org/10.1016/j.ejrh.2024.102058
153. Future changes in extreme precipitation from 1.0 °C more warming in the Tienshan Mountains, Central Asia X. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.128269
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787. Did European temperatures in 1540 exceed present-day records? R. Orth et al. https://doi.org/10.1088/1748-9326/11/11/114021
788. Impact of Extreme Heatwaves on Population Exposure in China Due to Additional Warming L. Wang et al. https://doi.org/10.3390/su141811458
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793. How Climate Change Affects Extremes in Maize and Wheat Yield in Two Cropping Regions C. Ummenhofer et al. https://doi.org/10.1175/JCLI-D-13-00326.1
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817. Evaluating Bias Correction Methods Using Annual Maximum Series Rainfall Data from Observed and Remotely Sensed Sources in Gauged and Ungauged Catchments in Uganda M. Okirya & J. Du Plessis https://doi.org/10.3390/hydrology12050113
818. Future Changes in Mean and Extreme Monsoon Precipitation in the Middle and Lower Yangtze River Basin, China, in the CMIP5 Models Y. Wu et al. https://doi.org/10.1175/JHM-D-16-0033.1
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820. Amplified potential for vegetation stress under climate-change-induced intensifying compound extreme events in the Greater Mediterranean Region P. Olschewski et al. https://doi.org/10.5194/nhess-24-1099-2024
821. Quality control and bias adjustment of crowdsourced wind speed observations J. Chen et al. https://doi.org/10.1002/qj.4146
822. 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
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830. Identifying future climate change and drought detection using CanESM2 in the upper Siem Reap River, Cambodia K. Chim et al. https://doi.org/10.1016/j.dynatmoce.2020.101182
831. Future Changes of European Windstorm Losses in EURO-CORDEX Simulations I. Alifdini et al. https://doi.org/10.16993/tellusa.4094
832. The effects of climate change on historical and future extreme rainfall in Antalya, Turkey A. Yilmaz https://doi.org/10.1080/02626667.2014.945455
833. Investigation of The Effect of Climate Change on Extreme Precipitation: Tekirdağ Case S. ORUC https://doi.org/10.31807/tjwsm.746134
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837. Water availability in Pakistan from Hindukush–Karakoram–Himalayan watersheds at 1.5 °C and 2 °C Paris Agreement targets S. Hasson et al. https://doi.org/10.1016/j.advwatres.2019.06.010
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840. A tree-ring based streamflow reconstruction for the Taohe River (Gannan Plateau, China) associated with the Asian-Pacific Oscillation since 1325 CE X. Shi et al. https://doi.org/10.1016/j.catena.2024.108027
841. The Use and Abuse of Climate Scenarios in Agriculture J. Kim & J. Yun https://doi.org/10.5532/KJAFM.2016.18.3.170
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843. Integrating Observational Datasets and CMIP6 Projections for Multi-Scale Drought Characterization: Evidence from Western Türkiye S. Aydemir et al. https://doi.org/10.3390/su18168543
844. From Flood to Drip Irrigation Under Climate Change: Impacts on Evapotranspiration and Groundwater Recharge in the Mediterranean Region of Valencia (Spain) S. Pool et al. https://doi.org/10.1029/2020EF001859
845. Future Changes in Precipitation Extremes over South Korea Based on Observations and CMIP6 SSP Scenarios S. Kim et al. https://doi.org/10.3390/w17111702
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848. Non-Stationary Hydrological Regimes Due to Climate Change: The Impact of Future Precipitation in the Spillway Design of a Reservoir, Case Study: Sube y Baja Dam, in Ecuador J. Herbozo et al. https://doi.org/10.3390/atmos13050828
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850. Projected trends in hydro-climatic extremes in small-to-mid-sized watersheds in eastern Nepal based on CMIP6 outputs A. Shrestha et al. https://doi.org/10.1007/s00382-023-06836-1
851. Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin G. Tefera et al. https://doi.org/10.3390/su151310513
852. Conditional and residual trends of singular hot days in Europe A. Jézéquel et al. https://doi.org/10.1088/1748-9326/ab76dd
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854. Raised bed furrow irrigation strategies and soil moisture dynamics for sustainable maize production in Bangladesh: Climate-informed DSSAT modeling under CMIP6 projections A. Adham et al. https://doi.org/10.1016/j.rineng.2026.111185
855. Estimation of probable maximum precipitation (PMP) in Hong Kong under future changing climate based on statistical downscaling S. Chan et al. https://doi.org/10.1016/j.uclim.2024.101988
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857. Present and future water scarcity in Switzerland: Potential for alleviation through reservoirs and lakes M. Brunner et al. https://doi.org/10.1016/j.scitotenv.2019.02.169
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859. Bias Correction of Paleoclimatic Reconstructions: A New Look at 1,200+ Years of Upper Colorado River Flow S. Robeson et al. https://doi.org/10.1029/2019GL086689
860. Climate change and hydrological analysis of Tekeze river basin Ethiopia: implication for potential hydropower production A. Adera & K. Alfredsen https://doi.org/10.2166/wcc.2019.203
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862. An ERA‐5 Derived CONUS‐Wide High‐Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework S. Emmanouil et al. https://doi.org/10.1029/2020WR029548
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865. Bias correcting regional scale Earth system model projections: novel approach using empirical mode decomposition A. Ganguli et al. https://doi.org/10.5194/gmd-18-8313-2025
866. Streamflow Reconstructions Using Tree-Ring-Based Paleo Proxies for the Sava River Basin (Slovenia) G. Tootle et al. https://doi.org/10.3390/hydrology10070138
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868. Evaluation and projection of CMIP6 simulations of climate variables for the Rift Valley Lakes Basin, Ethiopia Y. Woldemariam et al. https://doi.org/10.1007/s00704-025-05356-8
869. Projections of climatic extremes in a data poor transboundary river basin of India and Pakistan R. Aslam et al. https://doi.org/10.1002/joc.6501
870. Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes R. Kolachian & B. Saghafian https://doi.org/10.1007/s00704-018-2680-5
871. Changes in the risk of extreme temperatures in megacities worldwide C. Rajulapati et al. https://doi.org/10.1016/j.crm.2022.100433
872. Development of a Glacio-hydrological Model for Discharge and Mass Balance Reconstruction R. Kumar et al. https://doi.org/10.1007/s11269-016-1364-0
873. Historical and Future Drought Intensification in the Pantanal Wetland: Evidence from Multi-Source Weather Data and CMIP6 Multi-Model Projections J. Ernst et al. https://doi.org/10.3390/environments12110413
874. Integrated Assessment of Water Reservoir Systems Performance with the Implementation of Ecological Flows under Varying Climatic Conditions J. Vieira et al. https://doi.org/10.1007/s11269-018-2153-8
875. Influence of climate change on the ice conditions of the Curonian Lagoon D. Jakimavičius et al. https://doi.org/10.1016/j.oceano.2019.10.003
876. Integrating a hybrid machine learning model with climate projections for landslide susceptibility assessment M. Tumon et al. https://doi.org/10.1016/j.rineng.2025.108067
877. Atmosphere Driven Mass-Balance Sensitivity of Halji Glacier, Himalayas A. Arndt et al. https://doi.org/10.3390/atmos12040426
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879. Towards the development of an isotope‐enabled rainfall‐runoff model: Improving the ability to capture hydrological and anthropogenic change A. Watson et al. https://doi.org/10.1002/hyp.14819
880. Combined use of relative drought indices to analyze climate change impact on meteorological and hydrological droughts in a Mediterranean basin P. Marcos-Garcia et al. https://doi.org/10.1016/j.jhydrol.2017.09.028
881. Climate-smart irrigation planning for rabi maize (Zea mays L.): CMIP6 multi-model projections in north-central Bangladesh M. Islam et al. https://doi.org/10.1016/j.jafr.2025.102225
882. Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation models Y. Song et al. https://doi.org/10.1002/joc.7159
883. Extreme Rainfall and Temperature Changes Assessment on late-twenty-first-century over the Mono River Basin, Togo (West-Africa) B. Lamboni et al. https://doi.org/10.5194/piahs-383-69-2020
884. Modeling climate and land use change impacts on the hydrology of the Tiélé catchment, Benin, West Africa Q. Togbévi et al. https://doi.org/10.1007/s40808-026-02744-5
885. Spatiotemporal variability of future water sustainability using reliability resilience vulnerability framework Z. Hammad & E. Chung https://doi.org/10.1007/s00704-024-04949-z
886. Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean E. Maurer & D. Pierce https://doi.org/10.5194/hess-18-915-2014
887. Potential future impact of climate change on recharge in the Sierra de las Nieves (southern Spain) high-relief karst aquifer using regional climate models and statistical corrections E. Pardo-Igúzquiza et al. https://doi.org/10.1007/s12665-019-8594-4
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889. Quantile mapping bias correction methods to IMDAA reanalysis for calibrating NCMRWF unified model operational forecasts K. Niranjan Kumar et al. https://doi.org/10.1080/02626667.2022.2049272
890. On the use of climate models for estimating the non-stationary characteristic values of climatic actions in civil engineering practice L. Abrahamczyk & A. Uzair https://doi.org/10.3389/fbuil.2023.1108328
891. Development of the Niger Basin Drought Monitor (NBDM) for Early Warning and Concurrent Tracking of Meteorological, Agricultural and Hydrological Droughts J. Okpara et al. https://doi.org/10.3390/meteorology5010002
892. A novel bias correction method for extreme rainfall events based on L‐moments R. Anthanahalli Nanjegowda & S. Kulamulla Parambath https://doi.org/10.1002/joc.7242
893. Burning trees in frozen soil: Simulating fire, vegetation, soil, and hydrology in the boreal forests of Alaska M. Lucash et al. https://doi.org/10.1016/j.ecolmodel.2023.110367
894. Amazon savannization and climate change are projected to increase dry season length and temperature extremes over Brazil M. Bottino et al. https://doi.org/10.1038/s41598-024-55176-5
895. Bias-corrected UKCP18 Convection-permitting model projections for England Q. Zha et al. https://doi.org/10.5194/hess-30-2395-2026
896. A novel deep learning model for post-processing of short- and medium-term daily precipitation forecasts C. Zhao et al. https://doi.org/10.1016/j.atmosres.2025.108319
897. 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
898. Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods Y. Tong et al. https://doi.org/10.1007/s00382-020-05447-4
899. The skill of statistical downscaling in future climate with high-resolution climate models as pseudo-reality S. Mendoza Paz & P. Willems https://doi.org/10.1016/j.ejrh.2023.101477
900. Projected warming patterns across the Canadian prairies under CMIP6 scenarios: Terrain-informed bias-corrected temperature projections using machine learning Z. Noorisameleh & D. Sauchyn https://doi.org/10.1016/j.teadva.2026.200158
901. Selection and downscaling of CMIP6 climate models in Northern Nigeria I. Wada et al. https://doi.org/10.1007/s00704-023-04534-w
902. Assessing the Impacts of Land Cover and Climate Changes on Streamflow Dynamics in the Río Negro Basin (Colombia) Under Present and Future Scenarios B. Botero et al. https://doi.org/10.3390/hydrology12110281
903. Bias Correction and Statistical Modeling of Variable Oceanic Forcing of Greenland Outlet Glaciers V. Verjans et al. https://doi.org/10.1029/2023MS003610
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905. 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
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919. A fuzzy framework for risk analysis of dam-break flood in climate change scenarios A. Goel & V. Srinivas https://doi.org/10.1016/j.ijdrr.2025.105322
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921. Variation and uncertainty in the predicted flowering dates of cherry blossoms using the CMIP5 climate change scenario U. Chung et al. https://doi.org/10.1007/s13143-016-0033-9
922. Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information N. Pérez-Zanón et al. https://doi.org/10.5194/gmd-15-6115-2022
923. River runoff in Switzerland in a changing climate – runoff regime changes and their time of emergence R. Muelchi et al. https://doi.org/10.5194/hess-25-3071-2021
924. From calibration to real‐time operations: an assessment of three precipitation benchmarks for a Swiss river system N. Andres et al. https://doi.org/10.1002/met.1569
925. Superensembles of raw and bias‐adjusted regional climate models for Mediterranean region, Turkey B. Mesta & E. Kentel https://doi.org/10.1002/joc.7381
926. Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment D. Maraun et al. https://doi.org/10.1002/joc.5877
927. Future Changes in Drought Characteristics: Regional Analysis for South Korea under CMIP5 Projections J. Rhee & J. Cho https://doi.org/10.1175/JHM-D-15-0027.1
928. City-scale integrated flood risk prediction under future climate change and urbanization based on the shared socioeconomic pathways (SSP) scenarios W. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.132971
929. Performance evaluation of ERA5 precipitation estimates across Iran A. Malayeri et al. https://doi.org/10.1007/s12517-021-09079-8
930. Climate variability impacts on runoff projection under quantile mapping bias correction in the support CMIP6: An investigation in Lushi basin of China P. Xue et al. https://doi.org/10.1016/j.jhydrol.2022.128550
931. Managing climate change scenarios for societal impact studies G. Carbone https://doi.org/10.1080/02723646.2013.869714
932. A new approach to explore climate change impact on rainfall intensity–duration–frequency curves S. Soltani et al. https://doi.org/10.1007/s00704-020-03309-x
933. Effect of Climate Change on Hydrology, Sediment and Nutrient Losses in Two Lowland Catchments in Poland P. Marcinkowski et al. https://doi.org/10.3390/w9030156
934. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset S. Lange https://doi.org/10.5194/esd-9-627-2018
935. An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin Y. Luo et al. https://doi.org/10.1016/j.ejrh.2024.102146
936. Modelling the water balance of Lake Victoria (East Africa) – Part 2: Future projections I. Vanderkelen et al. https://doi.org/10.5194/hess-22-5527-2018
937. Uncertainty introduced by flood frequency analysis in projections for changes in flood magnitudes under a future climate in Norway D. Lawrence https://doi.org/10.1016/j.ejrh.2020.100675
938. Insights into uncertainties in future drought analysis using hydrological simulation model J. Kim & E. Chung https://doi.org/10.5194/hess-30-227-2026
939. Inundation modelling for Bangladeshi coasts using downscaled and bias-corrected temperature M. Hasan et al. https://doi.org/10.1016/j.crm.2019.100207
940. A Bernoulli-Gamma hierarchical Bayesian model for daily rainfall forecasts C. Lima et al. https://doi.org/10.1016/j.jhydrol.2021.126317
941. Selection of CMIP5 multi-model ensemble for the projection of spatial and temporal variability of rainfall in peninsular Malaysia M. Noor et al. https://doi.org/10.1007/s00704-019-02874-0
942. Time-varying effects of local weather on behavior and probability of breeding deferral in two Arctic-nesting goose populations S. Cunningham et al. https://doi.org/10.1007/s00442-022-05300-x
943. Possible NPP changes and risky ecosystem region identification in China during the 21st century based on BCC-CSM2 C. Zhang et al. https://doi.org/10.1007/s11442-020-1778-8
944. Future changes in crop yield over Poland driven by climate change, increasing atmospheric CO2 and nitrogen stress P. Marcinkowski & M. Piniewski https://doi.org/10.1016/j.agsy.2023.103813
945. Future exposure of rainfall and temperature extremes to the most populous island of Indonesia: A projection based on CORDEX simulation V. Hendrawan et al. https://doi.org/10.1002/joc.8537
946. Uncertainty of Rainfall Products: Impact on Modelling Household Nutrition from Rain-Fed Agriculture in Southern Africa R. Luetkemeier et al. https://doi.org/10.3390/w10040499
947. Machine-learning-enhanced multi-model climate ensemble for future rainfall, temperature and drought assessment in the semi-arid Papagni River Basin, India C. Naresh & A. Mathew https://doi.org/10.1016/j.pce.2026.104555
948. Changing controls on low flows in the Wiese catchment (southern Black Forest, Germany) under climate change S. Hoeg https://doi.org/10.1016/j.ejrh.2026.103957
949. Inter‐decadal variability in potential glacier surface melt energy at Vestari Hagafellsjökull (Langjökull, Iceland) and the role of synoptic circulation T. Matthews et al. https://doi.org/10.1002/joc.4191
950. Behaviour of energy piles under climate-change scenarios: a case study in Southern Italy M. Adinolfi et al. https://doi.org/10.1680/jenge.19.00093
951. Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand S. Wuthiwongtyohtin https://doi.org/10.2166/wcc.2020.021
952. 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
953. 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
954. Understanding the Uncertainty of the Lim River Basin Response to Changing Climate M. Stojkovic & S. Simonovic https://doi.org/10.1061/(ASCE)HE.1943-5584.0001964
955. Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods W. Qian & H. Chang https://doi.org/10.3390/ijerph18041992
956. Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India A. Vishwakarma et al. https://doi.org/10.2166/wpt.2022.098
957. Achieving effective calibration of precipitation forecasts over a continental scale Q. Yang et al. https://doi.org/10.1016/j.ejrh.2021.100818
958. Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires https://doi.org/10.1007/s10584-023-03597-y
959. The influence of natural variability and interpolation errors on bias characterization in RCM simulations N. Addor & E. Fischer https://doi.org/10.1002/2014JD022824
960. Precipitation Bias Correction: A Novel Semi‐parametric Quantile Mapping Method C. Rajulapati & S. Papalexiou https://doi.org/10.1029/2023EA002823
961. Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* M. Vrac & P. Friederichs https://doi.org/10.1175/JCLI-D-14-00059.1
962. Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths? J. Rajczak et al. https://doi.org/10.1175/JCLI-D-15-0162.1
963. Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010) S. Carreno-Madinabeitia et al. https://doi.org/10.1016/j.energy.2021.120364
964. 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
965. Validation and Projections of Climate Characteristics in the Saginaw Bay Watershed, MI, for Hydrologic Modeling Applications D. Kluver & W. Robertson https://doi.org/10.3389/frwa.2021.779811
966. A strategy to effectively make use of large volumes of climate data for climate change adaptation R. Benestad et al. https://doi.org/10.1016/j.cliser.2017.06.013
967. Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? P. Reiter et al. https://doi.org/10.1002/joc.5283
968. Hybrid approaches enhance hydrological model usability for local streamflow prediction Y. Du & I. Pechlivanidis https://doi.org/10.1038/s43247-025-02324-y
969. Impact of surface-net solar radiation and soil temperature on tea production in India: a study of the Dooars region in West Bengal P. Mallik & T. Ghosh https://doi.org/10.1007/s10113-021-01844-5
970. Rainfall Variability and Droughts in the Sebou Basin (Morocco): ERA5 Bias Correction by Quantile Mapping and Trend Analysis over 1980–2024 K. Rezkallah et al. https://doi.org/10.3390/app16147023
971. Spatio-temporal distribution of water availability in Karnali-Mohana Basin, Western Nepal: Climate change impact assessment (Part-B) V. Pandey et al. https://doi.org/10.1016/j.ejrh.2020.100691
972. Combined Dynamical and Statistical Downscaling for High-Resolution Projections of Multiple Climate Variables in the Beijing–Tianjin–Hebei Region of China Z. Han et al. https://doi.org/10.1175/JAMC-D-19-0050.1
973. Trends, Patterns, and Persistence of Rainfall, Streamflow, and Flooded Area in the Upper Paraguay Basin (Brazil) M. Coelho et al. https://doi.org/10.3390/w17101549
974. Modeling climate change impacts on streamflow dynamics in the Melka Wakana catchment, Ethiopia T. Geresu et al. https://doi.org/10.2166/wcc.2026.251
975. Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches S. Crimp et al. https://doi.org/10.1007/s00382-018-4188-1
976. Combining literature-based and data-driven fuzzy models to predict brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change R. Muñoz-Mas et al. https://doi.org/10.1016/j.ecolmodel.2018.08.012
977. New multivariate composite remote sensing drought index based on machine learning and geospatial techniques, insights from Northern Iraq K. Qaraghuli et al. https://doi.org/10.1016/j.ejrh.2026.103211
978. Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions M. Jemec Auflič et al. https://doi.org/10.1038/s41598-023-50314-x
979. Climate Change and Irrigation Effects on Hydrology and Crop Yield in the Geba Watershed, Tigray Region, Northern Ethiopia A. Meresa et al. https://doi.org/10.3390/hydrology13080208
980. Efficient photovoltaic power prediction to achieve carbon neutrality in China J. Gao et al. https://doi.org/10.1016/j.enconman.2025.119653
981. 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
982. Climate change impacts on wind power generation for the Italian peninsula R. Bonanno et al. https://doi.org/10.1007/s10113-022-02007-w
983. Identifying hotspots cities vulnerable to climate change in Pakistan under CMIP5 climate projections S. Ali et al. https://doi.org/10.1002/joc.6638
984. Enhancing climate change impact assessment in viticulture by resolving microclimates A. Fonseca et al. https://doi.org/10.1016/j.eja.2025.127740
985. Impact of climate change on potential evapotranspiration and crop water requirement in Upper Wabe Bridge watershed, Wabe Shebele River Basin, Ethiopia M. Gurara et al. https://doi.org/10.1016/j.jafrearsci.2021.104223
986. Projected changes in days with zero‐crossings for Norway I. Nilsen et al. https://doi.org/10.1002/joc.6913
987. Modeling climate change impacts on blue and green water of the Kobo-Golina River in data-scarce upper Danakil basin, Ethiopia B. Abate et al. https://doi.org/10.1016/j.ejrh.2024.101756
988. Hydrological response of Chamelia watershed in Mahakali Basin to climate change V. Pandey et al. https://doi.org/10.1016/j.scitotenv.2018.09.053
989. 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
990. Climate Change Adaptation Strategies for Canadian Asphalt Pavements; Part 1: Adaptation strategies S. Swarna et al. https://doi.org/10.1016/j.jclepro.2022.132313
991. Assessing future climate trends and their impact on different fractured rock aquifers in Northern Togo M. Akara et al. https://doi.org/10.2166/wcc.2022.264
992. Climate Change Impact on Hydrological Extremes: Preliminary Results from the Polish-Norwegian Project R. Romanowicz et al. https://doi.org/10.1515/acgeo-2016-0009
993. Climate change impact assessment on hydrological fluxes based on ensemble GCM outputs: a case study in eastern Indian River Basin J. Padhiary et al. https://doi.org/10.2166/wcc.2019.080
994. Aridity shifts in the MENA region under the Paris Agreement climate change scenarios M. Hamed et al. https://doi.org/10.1016/j.gloplacha.2024.104483
995. Watershed vulnerability assessment in the context of climate change, land use change, and water use change in the Nan River Basin, northern Thailand S. Talampas & S. Shrestha https://doi.org/10.2166/wcc.2025.546
996. Regression Recalibration by Learning PIT Map Values C. Qian et al. https://doi.org/10.1080/00401706.2025.2464004
997. Modelling high-resolution climate datasets for Djebel Aïssa National park (Southwestern Algeria) by downscaling worldclim data using GAM and RF I. Habibi et al. https://doi.org/10.1007/s40808-025-02593-8
998. Changes in storm surges based on a bias-adjusted reconstruction dataset from 1900 to 2010 J. Feng et al. https://doi.org/10.1016/j.jhydrol.2022.128759
999. Future changes of summer monsoon rainfall and temperature over Bangladesh using 27 CMIP6 models A. Bhattacharjee et al. https://doi.org/10.1080/10106049.2023.2285342
1000. 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
1001. Integrating Climate Modelling, Downscaling, and Bias Correction for Drought Studies: Current Approaches and Future Directions C. Naresh & A. Mathew https://doi.org/10.1007/s11269-026-04843-5
1002. Risks and opportunities for a Swiss hydroelectricity company in a changing climate K. Hakala et al. https://doi.org/10.5194/hess-24-3815-2020
1003. Sensitivities of heat-wave mortality projections: Moving towards stochastic model assumptions L. Abadie & J. Polanco-Martínez https://doi.org/10.1016/j.envres.2021.111895
1004. Extreme precipitation events under climate change in the Iberian Peninsula S. Cardoso Pereira et al. https://doi.org/10.1002/joc.6269
1005. An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts A. Collados-Lara et al. https://doi.org/10.5194/nhess-22-599-2022
1006. 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