29 citations as recorded by crossref.

1. Statistical Postprocessing of High-Resolution Regional Climate Model Output P. Mendoza et al. https://doi.org/10.1175/MWR-D-14-00159.1
2. Towards Reducing Climate Change Impact Assessment Process Uncertainty A. Gaur & S. Simonovic https://doi.org/10.1007/s40710-015-0070-x
3. Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method S. Ngai et al. https://doi.org/10.1016/j.gloplacha.2016.12.009
4. Selection of climate simulations for climate change impact studies: case study of the Souss watershed, Morocco M. Meliho et al. https://doi.org/10.1007/s00704-025-05353-x
5. Assessment of an analogue downscaling method for modelling climate change impacts on runoff J. Teng et al. https://doi.org/10.1016/j.jhydrol.2012.09.024
6. Constraining Climate Model Projections of Regional Precipitation Change B. Zhang & B. Soden https://doi.org/10.1029/2019GL083926
7. Storylines for the 1997 New Year’s Flood: The role of watershed antecedent conditions and future warming in shaping discharge in the Truckee River watershed G. Yu et al. https://doi.org/10.1016/j.jhydrol.2026.135080
8. Correcting for model changes in statistical postprocessing – an approach based on response theory J. Demaeyer & S. Vannitsem https://doi.org/10.5194/npg-27-307-2020
9. Evaluating the Time-Invariance Hypothesis of Climate Model Bias Correction: Implications for Hydrological Impact Studies J. Velázquez et al. https://doi.org/10.1175/JHM-D-14-0159.1
10. Improving the Reliability and Added Value of Dynamical Downscaling via Correction of Large‐Scale Errors: A Norwegian Perspective M. Pontoppidan et al. https://doi.org/10.1029/2018JD028372
11. Assessment of calibration assumptions under strong climate changes B. Van Schaeybroeck & S. Vannitsem https://doi.org/10.1002/2016GL067721
12. Projected Changes to Extreme Runoff and Precipitation Events From a Downscaled Simulation Over the Western United States M. Wrzesien & T. Pavelsky https://doi.org/10.3389/feart.2019.00355
13. Downscaling fire weather extremes from historical and projected climate models P. Jain et al. https://doi.org/10.1007/s10584-020-02865-5
14. HESS Opinions "Should we apply bias correction to global and regional climate model data?" U. Ehret et al. https://doi.org/10.5194/hess-16-3391-2012
15. Climate Change in the Mediterranean Basin (Part II): A Review of Challenges and Uncertainties in Climate Change Modeling and Impact Analyses L. Noto et al. https://doi.org/10.1007/s11269-023-03444-w
16. Multivariate bias corrections of mechanistic water quality model predictions D. Libera & A. Sankarasubramanian https://doi.org/10.1016/j.jhydrol.2018.07.043
17. A multi-perspective approach for selecting CMIP6 scenarios to project climate change impacts on glacio-hydrology with a case study in Upper Indus river basin M. Shafeeque & Y. Luo https://doi.org/10.1016/j.jhydrol.2021.126466
18. Towards process-informed bias correction of climate change simulations D. Maraun et al. https://doi.org/10.1038/nclimate3418
19. Bias Correcting Climate Change Simulations - a Critical Review D. Maraun https://doi.org/10.1007/s40641-016-0050-x
20. Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain A. Tsegaw et al. https://doi.org/10.5194/nhess-20-2133-2020
21. Data-driven techniques in rainfall forecasting using CMIP6 simulation outputs and ground-observed data V. Dubey & V. Vidyarthi https://doi.org/10.1007/s11600-025-01626-1
22. What is the importance of climate model bias when projecting the impacts of climate change on land surface processes? M. Liu et al. https://doi.org/10.5194/bg-11-2601-2014
23. How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC) J. Romero-Cuellar & F. Francés https://doi.org/10.1080/02626667.2023.2231437
24. Evolution of urban heat wave intensity for the Brussels Capital Region in the ARPEGE-Climat A1B scenario R. Hamdi et al. https://doi.org/10.1016/j.uclim.2016.08.001
25. The Upper Tail of Precipitation in Convection‐Permitting Regional Climate Models and Their Utility in Nonstationary Rainfall and Flood Frequency Analysis G. Yu et al. https://doi.org/10.1029/2020EF001613
26. 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
27. Low-frequency variability and heat transport in a low-order nonlinear coupled ocean–atmosphere model S. Vannitsem et al. https://doi.org/10.1016/j.physd.2015.07.006
28. Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB) A. Khan & M. Koch https://doi.org/10.3390/cli6040089
29. Comparative analysis of bias correction techniques for future climate assessment using CMIP6 hydrological variables for the Indian subcontinent M. Shah et al. https://doi.org/10.1007/s11600-024-01378-4