CMIP6印度高分辨率多模式多情景降水数据集

该数据集呈现了一个多模型多场景网格（0.25°×0.25°）偏差校正的印度未来预测日降水序列。在使用Maity等人（2019）最近开发的基于copula的偏差校正技术（称为RMPH方法）校正偏差后，使用了14种最先进的CMIP6-GCM来开发该数据集。对于每种模型，都使用了三种场景——历史、SSP245和SSP585。此外，传统的分位数映射（QM）方法也用于比较，但仅限于历史时期（1961-2014）。
CMIP6的新偏差校正降水数据集对于未来的风险评估研究、水文模拟、制定气候变化适应和缓解策略下的极端事件特别有用。
有关更多详细信息，读者可以参考以下出版物：
Sarkar S.、S.S Maity和R.Maity（2023），通过CMIP6的多模型评估，印度各地基于降水的气候变化热点，《水文学杂志》，爱思唯尔，https://doi.org/10.1016/j.jhydrol.2023.129805

This dataset presents a bias-corrected daily precipitation forecast series for India, based on a multi-model and multi-scenario grid (0.25°×0.25°). It was developed using 14 state-of-the-art CMIP6-GCMs, following bias correction via the copula-based bias correction technique (named the RMPH method) recently proposed by Maity et al. (2019). Three scenarios—historical, SSP245, and SSP585—were employed for each model. Additionally, the traditional Quantile Mapping (QM) method was used for comparison, but only during the historical period (1961–2014).

This novel CMIP6-based bias-corrected precipitation dataset is particularly valuable for future risk assessment studies, hydrological simulations, and the development of extreme event adaptation and mitigation strategies under climate change.

For more details, readers may refer to the following publication:
Sarkar S., S.S. Maity, and R. Maity (2023). Multi-model assessment of precipitation-based climate change hotspots across India using CMIP6. *Journal of Hydrology*, Elsevier. https://doi.org/10.1016/j.jhydrol.2023.129805