37 citations as recorded by crossref.

1. A new bias-correction method for precipitation over complex terrain suitable for different climate states: a case study using WRF (version 3.8.1) P. Velasquez et al. 10.5194/gmd-13-5007-2020
2. Learning to Correct Climate Projection Biases B. Pan et al. 10.1029/2021MS002509
3. Projected changes in rainfall amount and distribution in the Democratic Republic of Congo – Evidence from an ensemble of high-resolution climate simulations F. Bangelesa et al. 10.1016/j.wace.2023.100620
4. Research on Meteorological Drought Risk Prediction in the Daqing River Basin Based on HADGEM3-RA M. Lv & Z. Wang 10.3390/agriculture14101781
5. Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics D. Faranda et al. 10.1007/s00382-023-06794-8
6. MIdASv0.2.1 – MultI-scale bias AdjuStment P. Berg et al. 10.5194/gmd-15-6165-2022
7. 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. 10.1002/joc.7272
8. Projected mean and extreme precipitation based on bias-corrected simulation outputs of CORDEX Southeast Asia S. Ngai et al. 10.1016/j.wace.2022.100484
9. Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective V. de Padua & K. Ahn 10.1016/j.jhydrol.2024.132102
10. Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate F. Tootoonchi et al. 10.1016/j.scitotenv.2022.158615
11. Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment) S. Marahatta et al. 10.3390/w13111548
12. Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models P. Deng & J. Zhu 10.1007/s00704-021-03660-7
13. Climate Change Projections of Extreme Temperatures for the Iberian Peninsula C. Viceto et al. 10.3390/atmos10050229
14. Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study M. Hofer & J. Horak 10.3390/atmos11111256
15. Predictability of water resources with global climate models. Case of Northern Tunisia M. Besbes & J. Chahed 10.5802/crgeos.219
16. Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
17. Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods L. Guo et al. 10.1007/s10584-020-02841-z
18. Evaluation of Quantile Delta Mapping as a bias‐correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil) A. Xavier et al. 10.1002/joc.7238
19. Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments A. Sharma et al. 10.2166/wcc.2023.230
20. Towards a More Robust Evaluation of Climate Model and Hydrological Impact Uncertainties E. Pastén-Zapata et al. 10.1007/s11269-022-03212-2
21. Multivariate stochastic bias corrections with optimal transport Y. Robin et al. 10.5194/hess-23-773-2019
22. Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study R. Bonnet et al. 10.1088/2752-5295/ac6adc
23. Projection of future precipitation extremes across the Bangkok Metropolitan Region R. Cooper 10.1016/j.heliyon.2019.e01678
24. Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling J. Van de Velde et al. 10.3390/w13111573
25. Normalized coefficients of prediction accuracy for comparative forecast verification and modeling G. Müller-Plath & H. Lüdecke 10.1080/27684520.2024.2317172
26. Standard assessments of climate forecast skill can be misleading J. Risbey et al. 10.1038/s41467-021-23771-z
27. 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. 10.5194/hess-26-2319-2022
28. The effect of univariate bias adjustment on multivariate hazard estimates J. Zscheischler et al. 10.5194/esd-10-31-2019
29. Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought H. Tabari et al. 10.5194/hess-25-3493-2021
30. 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. 10.5194/gmd-17-1249-2024
31. Assessing internal changes in the future structure of dry–hot compound events: the case of the Pyrenees M. Lemus-Canovas & J. Lopez-Bustins 10.5194/nhess-21-1721-2021
32. Impact of downscaled rainfall biases on projected runoff changes S. Charles et al. 10.5194/hess-24-2981-2020
33. Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments J. Meyer et al. 10.5194/hess-23-1339-2019
34. Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi R. Manzanas et al. 10.1007/s10584-020-02867-3
35. Assessment of plausible changes in Climatic Impact-Drivers relevant for the viticulture sector: A storyline approach with a climate service perspective J. Mindlin et al. 10.1016/j.cliser.2024.100480
36. A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model J. Jeong & S. Lee 10.3390/atmos9080291
37. Time Scale Decomposition of Climate and Correction of Variability Using Synthetic Samples of Stable Distributions M. Gomez‐Garcia et al. 10.1029/2018WR023053