A High-resolution Multi-model Multi-scenario Precipitation Dataset across India from CMIP6

This dataset presents a multi-model multi-scenario gridded (0.25°×0.25°) bias-corrected future projected daily precipitation series across India. Fourteen state-of-the-art CMIP6 GCMs have been used to develop this dataset, after correcting the bias using a recently developed copula-based bias-correction technique by Maity et al. (2019), referred as RMPH method. For each of the models, three scenarios have been used -- Historical, SSP245 and SSP585. Additionally, the conventiinal Quantile Mapping (QM) method is also for comparison, but only over the historical period (1961-2014).

The new bias-corrected precipitation dataset from CMIP6 is particularly useful for the future risk assessment studies, hydrological simulations, formulating extreme events under climate change adaptation and mitigation strategies.

For further details, readers can refer to the following publication:

Sarkar S., S. S Maity, and R. Maity (2023), Precipitation-based Climate Change Hotspots across India through a Multi-model Assessment from CMIP6, Journal of Hydrology, Elsevier, https://doi.org/10.1016/j.jhydrol.2023.129805

本数据集涵盖印度全域的多模式多情景网格化（0.25°×0.25°）未来预估逐日降水序列，且已完成偏差校正。本数据集基于14个最先进的耦合模式比较计划第六阶段（CMIP6）全球气候模式（General Circulation Model, GCM）构建，构建前采用Maity等（2019）提出的新型基于Copula函数的偏差校正技术（下称RMPH方法）对模式降水偏差进行了校正。针对每个模式，均设置了历史情景（Historical）、SSP245情景与SSP585情景三类试验方案。此外，为开展对比分析，本数据集还采用了传统的分位数映射法（Quantile Mapping, QM）进行偏差校正，但仅覆盖历史时段（1961-2014年）。

该新型经偏差校正的CMIP6降水数据集，可有效支撑未来气候风险评估、水文模拟，以及气候变化适应与减缓战略框架下的极端事件应对研究。

如需了解更多细节，读者可参阅以下文献：
Sarkar S.、Maity S.S.与Maity R.（2023）：《基于CMIP6多模式评估的印度全域降水型气候变化热点区域》，刊载于《Journal of Hydrology》（爱思唯尔（Elsevier）出版），DOI：10.1016/j.jhydrol.2023.129805