73 citations as recorded by crossref.

1. Verification and Bias Adjustment of ECMWF SEAS5 Seasonal Forecasts over Europe for Climate Service Applications A. Crespi et al. https://doi.org/10.3390/cli9120181
2. Differential Impacts of 1.5 and 2 °C Warming on Extreme Events Over China Using Statistically Downscaled and Bias‐Corrected CESM Low‐Warming Experiment Y. Yang et al. https://doi.org/10.1029/2018GL079272
3. Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach S. Monhart et al. https://doi.org/10.5194/hess-23-493-2019
4. 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
5. The Value of Subseasonal Hydrometeorological Forecasts to Hydropower Operations: How Much Does Preprocessing Matter? D. Anghileri et al. https://doi.org/10.1029/2019WR025280
6. Bridging the scale gap: obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate Q. Yuan et al. https://doi.org/10.5194/hess-25-5259-2021
7. The VALUE perfect predictor experiment: Evaluation of temporal variability D. Maraun et al. https://doi.org/10.1002/joc.5222
8. 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
9. Changes in mean and extreme temperature and precipitation events from different weighted multi-model ensembles over the northern half of Morocco S. Balhane et al. https://doi.org/10.1007/s00382-021-05910-w
10. Convolutional conditional neural processes for local climate downscaling A. Vaughan et al. https://doi.org/10.5194/gmd-15-251-2022
11. Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand S. Wuthiwongtyohtin https://doi.org/10.2166/wcc.2020.021
12. A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation J. Schmidt et al. https://doi.org/10.1038/s41612-025-01157-y
13. Evaluation of Bias-Corrected GCM CMIP6 Simulation of Sea Surface Temperature over the Gulf of Guinea O. Ideki & A. Lupo https://doi.org/10.3390/cli12020019
14. 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
15. An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment J. Gutiérrez et al. https://doi.org/10.1002/joc.5462
16. 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
17. Sparse Canonical Correlation Analysis Postprocessing Algorithms for GCM Daily Rainfall Forecasts C. Lima et al. https://doi.org/10.1175/JHM-D-21-0233.1
18. 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
19. Technical note: Including hydrologic impact definition in climate projection uncertainty partitioning: a case study of the Central American mid-summer drought E. Maurer & I. Stewart https://doi.org/10.5194/hess-30-421-2026
20. Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation H. Ji et al. https://doi.org/10.1016/j.envsoft.2023.105896
21. Towards process-informed bias correction of climate change simulations D. Maraun et al. https://doi.org/10.1038/nclimate3418
22. Reanalysis Product-Based Nonstationary Frequency Analysis for Estimating Extreme Design Rainfall D. Kim et al. https://doi.org/10.3390/atmos12020191
23. Climate model downscaling in central Asia: a dynamical and a neural network approach B. Fallah et al. https://doi.org/10.5194/gmd-18-161-2025
24. Exploring machine learning approaches for precipitation downscaling H. Zhu et al. https://doi.org/10.1080/10095020.2025.2477547
25. 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
26. Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction M. Vrac https://doi.org/10.5194/hess-22-3175-2018
27. Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events T. Economou & F. Garry https://doi.org/10.1016/j.wace.2022.100522
28. Stochastic downscaling of gridded precipitation to spatially coherent subgrid precipitation fields using a transformed Gaussian model M. Switanek et al. https://doi.org/10.1002/joc.7581
29. Nonstationary stochastic rain type generation: accounting for climate drivers L. Benoit et al. https://doi.org/10.5194/hess-24-2841-2020
30. Multi-model approach for statistical downscaling and bias correction of Earth system model outputs for marine ecosystem impact applications R. Oliveros-Ramos et al. https://doi.org/10.1093/icesjms/fsaf073
31. Leveraging reforecasts for flood estimation with long continuous simulation: a proof-of-concept study D. Viviroli et al. https://doi.org/10.5194/nhess-26-1835-2026
32. 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
33. 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
34. Evaluation of the Accuracy and Applicability of Reanalysis Precipitation Products in the Lower Yarlung Zangbo Basin A. Tan et al. https://doi.org/10.3390/rs17142396
35. An ensemble-based projection of future hydro-climatic extremes in Iran A. Jahanshahi et al. https://doi.org/10.1016/j.jhydrol.2024.131892
36. Exploration of Daily Rainfall Intensity Change in South Korea 1900–2010 Using Bias-Corrected ERA-20C D. Kim et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001928
37. Comparison of statistical downscaling methods with respect to extreme events over Europe: Validation results from the perfect predictor experiment of the COST Action VALUE E. Hertig et al. https://doi.org/10.1002/joc.5469
38. Copula‐based downscaling of daily precipitation fields M. Lorenz et al. https://doi.org/10.1002/hyp.13271
39. Bias correction of daily precipitation over South Korea from the long-term reanalysis using a composite Gamma-Pareto distribution approach D. Kim et al. https://doi.org/10.2166/nh.2019.127
40. Continental-scale bias correction and random forest downscaling of CMIP6 precipitation across Europe S. Doshi et al. https://doi.org/10.1080/10106049.2026.2657618
41. Changes in temperature–precipitation correlations over Europe: are climate models reliable? M. Vrac et al. https://doi.org/10.1007/s00382-022-06436-5
42. An integral assessment of carbon and nitrogen emissions in dairy cattle production systems: Comparing dynamic process-based greenhouse gas emissions factors with IPCC Tier 1 and Tier 2 approaches in confinement and pasture-based systems L. Ouatahar et al. https://doi.org/10.1016/j.jclepro.2024.144479
43. Classificação Climática de Thornthwaite para o Brasil com Base em Cenários de Mudanças Climáticas do IPCC-AR5 A. Marcos Junior et al. https://doi.org/10.1590/0102-7786334007
44. Projections of climate change in streamflow and affluent natural energy in the Brazilian hydroelectric sector of CORDEX models M. Silva et al. https://doi.org/10.1590/2318-0331.252020200020
45. Bias correction of surface air temperature and precipitation in CORDEX East Asia simulation: What should we do when applying bias correction? J. Chen et al. https://doi.org/10.1016/j.atmosres.2022.106439
46. Bias Correction of Surface Air Temperature and Precipitation in Cordex East Asia Simulation: What Should We Do When Applying Bias Correction? J. Chen et al. https://doi.org/10.2139/ssrn.4092894
47. Stochastic daily rainfall generation on tropical islands with complex topography L. Benoit et al. https://doi.org/10.5194/hess-26-2113-2022
48. Remaining error sources in bias-corrected climate model outputs J. Chen et al. https://doi.org/10.1007/s10584-020-02744-z
49. Probabilistic downscaling of EURO-CORDEX precipitation data for the assessment of future areal precipitation extremes for hourly to daily durations A. El Hachem et al. https://doi.org/10.5194/hess-29-1335-2025
50. Advances and challenges in climate modeling O. Alizadeh https://doi.org/10.1007/s10584-021-03298-4
51. 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
52. 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
53. Projected Changes in the Atmospheric Dynamics of Climate Extremes in France P. Yiou et al. https://doi.org/10.3390/atmos12111440
54. Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America Q. Guo et al. https://doi.org/10.1029/2019WR026659
55. Climate change impact on blue and green water resources distributions in the Beijiang River basin based on CORDEX projections C. Dai et al. https://doi.org/10.2166/wcc.2022.115
56. Changes in Future Synoptic Circulation Patterns: Consequences for Extreme Event Attribution D. Faranda et al. https://doi.org/10.1029/2020GL088002
57. 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
58. New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid Q. Yuan et al. https://doi.org/10.1175/JAMC-D-19-0086.1
59. A new multi-site multi-variable stochastic model with inter-site and inter-variable correlations, low frequency attributes and stochasticity: A case study in the lower Yellow River basin Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2021.126365
60. Bias correction of daily precipitation for ungauged locations using geostatistical approaches: A case study for the CORDEX‐Africa ensemble M. Lorenz et al. https://doi.org/10.1002/joc.7649
61. Study of climate change impact on hydro-climatic extremes in the Hanjiang River basin, China, using CORDEX-EAS data C. Dai et al. https://doi.org/10.1016/j.wace.2022.100509
62. Projected bioclimatic shifts in Pakistan: A CMIP6 ensemble analysis under shared socioeconomic pathways W. Khan et al. https://doi.org/10.1007/s00704-025-05901-5
63. On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs A. Schepen et al. https://doi.org/10.1175/MWR-D-19-0046.1
64. Probabilistic Simulationof Big Climate Data for Robust Quantification of Changes in Compound Hazard Events T. Economou & F. Garry https://doi.org/10.2139/ssrn.4106219
65. 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
66. Time Scale Decomposition of Climate and Correction of Variability Using Synthetic Samples of Stable Distributions M. Gomez‐Garcia et al. https://doi.org/10.1029/2018WR023053
67. 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
68. Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis D. Khedhaouiria et al. https://doi.org/10.1007/s00477-019-01722-x
69. 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
70. A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks Y. Kim et al. https://doi.org/10.1029/2021GL095729
71. 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
72. The stationarity of two statistical downscaling methods for precipitation under different choices of cross‐validation periods Y. Wang et al. https://doi.org/10.1002/joc.5375
73. Revisiting the bias correction of climate models for impact studies T. Dinh & F. Aires https://doi.org/10.1007/s10584-023-03597-y