12 citations as recorded by crossref.

1. Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts D. Gergel et al. 10.5194/gmd-17-191-2024
2. Bias‐Adjustment Methods for Future Subdaily Precipitation Extremes Consistent Across Durations H. Van de Vyver et al. 10.1029/2022EA002798
3. Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. 10.1080/02626667.2022.2144322
4. Impact of diverse configuration in multivariate bias correction methods on large-scale hydrological modelling under climate change K. Ahn et al. 10.1016/j.jhydrol.2023.130406
5. 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
6. Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula A. Ugolotti et al. 10.3389/fenvs.2023.1116429
7. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. 10.1016/j.earscirev.2022.104241
8. Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin R. Ansari et al. 10.5194/gmd-16-2055-2023
9. Water Resources Evaluation and Sustainability Considering Climate Change and Future Anthropic Demands in the Arequipa Region of Southern Peru J. Quiroz et al. 10.3390/su152316270
10. Assessing the impact of bias correction approaches on climate extremes and the climate change signal H. Zhang et al. 10.1002/met.2204
11. 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
12. On deep learning-based bias correction and downscaling of multiple climate models simulations F. Wang & D. Tian 10.1007/s00382-022-06277-2