59 citations as recorded by crossref.

1. The effect of bias correction and climate model resolution on wheat simulations forced with a regional climate model ensemble I. Macadam et al. https://doi.org/10.1002/joc.4653
2. 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
3. Predictability of moisture flux anomalies indicating central European extreme precipitation events B. Gvoždíková & M. Müller https://doi.org/10.1002/qj.4131
4. Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling Y. Zheng et al. https://doi.org/10.1007/s00376-023-3118-2
5. Correcting systematic biases across multiple atmospheric variables in the frequency domain H. Nguyen et al. https://doi.org/10.1007/s00382-018-4191-6
6. 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
7. Dry GCMs vs. wet RCMs: precipitation biases over Southern Italy A. Izzaddin et al. https://doi.org/10.1007/s00477-025-03022-z
8. Optimizing Regional Climate Model Output for Hydro-Climate Applications in the Eastern Nile Basin M. Osman et al. https://doi.org/10.1007/s41748-021-00222-9
9. Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling J. Van de Velde et al. https://doi.org/10.3390/w13111573
10. Seasonal Bias Correction of Daily Precipitation over France Using a Stitch Model Designed for Robust Representation of Extremes P. Ear et al. https://doi.org/10.3390/atmos16040480
11. Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. https://doi.org/10.5194/hess-26-2319-2022
12. Climate Change and Rainfall Intensity–Duration–Frequency Curves: Overview of Science and Guidelines for Adaptation J. Martel et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002122
13. The importance of artificial intelligence-based methods in precipitation modeling studies: a bibliometric analysis O. Aydin & H. Kilar https://doi.org/10.1007/s00704-025-05837-w
14. Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin R. Ansari et al. https://doi.org/10.5194/gmd-16-2055-2023
15. Observed decrease in light precipitation in part due to urbanization S. Tysa & G. Ren https://doi.org/10.1038/s41598-022-07897-8
16. An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment P. Smitha et al. https://doi.org/10.1016/j.jhydrol.2017.11.010
17. Multi-Criteria Decision Framework to Evaluate Bias Corrected Climate Change Projections in the Piracicaba River Basin C. Billerbeck et al. https://doi.org/10.1590/0102-77863630068
18. A comparison of methods to estimate future sub-daily design rainfall J. Li et al. https://doi.org/10.1016/j.advwatres.2017.10.020
19. 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
20. Long-term trends and projections of climate variability for the Borkena river catchment of the Awash River basin, Ethiopia T. Kifle Wondie & G. Sintayehu Angualie https://doi.org/10.1007/s42452-025-07439-7
21. A parametric approach for simultaneous bias correction and high‐resolution downscaling of climate model rainfall A. Mamalakis et al. https://doi.org/10.1002/2016WR019578
22. 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
23. Climate change impact on flood hazard over Italy M. García-Valdecasas Ojeda et al. https://doi.org/10.1016/j.jhydrol.2022.128628
24. Convection‐permitting modeling with regional climate models: Latest developments and next steps P. Lucas‐Picher et al. https://doi.org/10.1002/wcc.731
25. Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter M. Vrac et al. https://doi.org/10.1002/2015JD024511
26. A dual-TransUNet deep learning framework for multi-source precipitation merging and improving seasonal and extreme estimates Y. Ye et al. https://doi.org/10.1016/j.ejrh.2026.103453
27. Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations A. Casanueva et al. https://doi.org/10.1007/s00382-015-2865-x
28. 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
29. Fidelity of WRF model in simulating heat wave events over India P. Gupta et al. https://doi.org/10.1038/s41598-024-52541-2
30. Performance of the general circulation models in simulating temperature and precipitation over Iran M. Abbasian et al. https://doi.org/10.1007/s00704-018-2456-y
31. Projection of future precipitation extremes across the Bangkok Metropolitan Region R. Cooper https://doi.org/10.1016/j.heliyon.2019.e01678
32. Effects of City Expansion on Heat Stress under Climate Change Conditions D. Argüeso et al. https://doi.org/10.1371/journal.pone.0117066
33. Statistical bias correction for creating coherent total ozone record from OMI and OMPS observations K. Bai et al. https://doi.org/10.1016/j.rse.2016.05.007
34. 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
35. 2D and 3D urban morphology effects on land surface temperature and thermal comfort in arid environments G. Halima et al. https://doi.org/10.1016/j.ejrs.2026.04.005
36. Propagation of climate model biases to biophysical modelling can complicate assessments of climate change impact in agricultural systems D. Liu et al. https://doi.org/10.1002/joc.5820
37. Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.133213
38. A non-stationary bias adjustment method for improving the inter-annual variability and persistence of projected precipitation M. Cantalejo et al. https://doi.org/10.1038/s41598-024-76848-2
39. Keeping Us Honest: Examining Climate States and Transition Probabilities of Precipitation Projections in General Circulation Models T. Panaou et al. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000910
40. Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective V. de Padua & K. Ahn https://doi.org/10.1016/j.jhydrol.2024.132102
41. Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections A. Luca et al. https://doi.org/10.1007/s00382-017-3946-9
42. CMIP5‐predicted climate shifts over the East Mediterranean: implications for the transition region between Mediterranean and semi‐arid climates N. Peleg et al. https://doi.org/10.1002/joc.4114
43. High-resolution bias-corrected precipitation data over South Siberia, Russia N. Voropay et al. https://doi.org/10.1016/j.atmosres.2021.105528
44. Can Regional Climate Modeling Capture the Observed Changes in Spatial Organization of Extreme Storms at Higher Temperatures? J. Li et al. https://doi.org/10.1029/2018GL077716
45. Projected changes of precipitation extremes in North America using CMIP6 multi-climate model ensembles J. Zhao et al. https://doi.org/10.1016/j.jhydrol.2023.129598
46. Modelling Impacts of Climate Change on a River Basin: Analysis of Uncertainty Using REA & Possibilistic Approach J. Das et al. https://doi.org/10.1007/s11269-018-2046-x
47. Heatwaves intensification in Australia: A consistent trajectory across past, present and future R. Trancoso et al. https://doi.org/10.1016/j.scitotenv.2020.140521
48. Spatio-Temporal Variation of Water Availability in a River Basin under CORDEX Simulated Future Projections J. Das & N. Umamahesh https://doi.org/10.1007/s11269-017-1876-2
49. Seasonal precipitation prediction over Vietnam: evaluation of RegCM dynamical downscaling and statistical bias correction of NCEP CFS forecasts H. Pham-Thanh et al. https://doi.org/10.1007/s00704-026-06117-x
50. How does bias correction of regional climate model precipitation affect modelled runoff? J. Teng et al. https://doi.org/10.5194/hess-19-711-2015
51. Modelling climate change impacts on nutrients and primary production in coastal waters M. Pesce et al. https://doi.org/10.1016/j.scitotenv.2018.02.131
52. Quantifying future changes of flood hazards within the Broadland catchment in the UK R. Gudde et al. https://doi.org/10.1007/s11069-024-06590-5
53. Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region G. Salem et al. https://doi.org/10.1016/j.agwat.2018.06.011
54. Regional modeling of groundwater recharge in the Basin of Mexico: new insights from satellite observations and global data sources S. González-Ortigoza et al. https://doi.org/10.1007/s10040-023-02667-w
55. Climate scenarios of extreme precipitation using a combination of parametric and non-parametric bias correction methods in the province of Québec P. Roy et al. https://doi.org/10.1080/07011784.2023.2220682
56. An Analysis of Regional Climate Simulations for Western Australia’s Wine Regions—Model Evaluation and Future Climate Projections R. Firth et al. https://doi.org/10.1175/JAMC-D-16-0333.1
57. Optimal reservoir operation – A climate change adaptation strategy for Narmada basin in central India T. Thomas et al. https://doi.org/10.1016/j.jhydrol.2021.126238
58. A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM J. Li et al. https://doi.org/10.1016/j.jhydrol.2017.02.019
59. Demonstrating the bias-correction impact on regional climate model (RegCM) over the Democratic People’s Republic of Korea: Implication for temperature and precipitation K. Jo & K. Pak https://doi.org/10.1007/s12040-022-01836-x