The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.

File naming and required software

The data files are stored separately by stations, and the extreme precipitation index data is stored in Excel format with the name of“ yyyyy.xlsx ”Where yyyyy represents the WMO site number. Convenient for subsequent processing and application, users can selectively download data according to the actual situation.

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Cite as:

Yao, J., Chen, J., Li, J. (2021). Long time series data set of extreme precipitation index in arid region of Central Asia. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.271138. CSTR: 18406.11.Meteoro.tpdc.271138. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Yao, J., Chen, Y., Chen, J., Zhao, Y., & Mao, W. (2020). Intensification of extreme precipitation in arid central asia. Journal of Hydrology, 125760. doi.org/10.1016/j.jhydrol.2020.125760( View Details | Download | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Support Program

Second Tibetan Plateau Scientific Expedition Program

National Key Research and Development Program of China (No:2018YFC1507101)

National Natural Science Foundation of China (No:U1903113)

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)