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Challenges and potential solutions in statistical downscaling of precipitation

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Abstract

Downscaling is an effective technique to bridge the gap between climate model outputs and data requirements of most crop and hydrologic models for assessing local and site-specific climate change impacts, especially on future food security. However, downscaling of temporal sequences, extremes in daily precipitation, and handling of nonstationary precipitation in future conditions are considered common challenges for most statistical downscaling methods. This study reviewed the above key challenges in statistical downscaling and proposed potential solutions. Ten weather stations located across the globe were used as proof of concept. The use of a stochastic Markov chain to generate daily precipitation occurrences is an effective approach to simulate the temporal sequence of precipitation. Also, the downscaling of precipitation extremes can be achieved by adjusting the skewness coefficient of a probability distribution, as they are highly correlated. Nonstationarity in precipitation downscaling can be handled by adjusting parameters of a probability distribution according to future precipitation change signals projected by climate models. The perspectives proposed in this study are of great significance in using climate model outputs for assessing local and site-specific climate change impacts, especially on future food security.

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Data availability

The data for ten stations are not publicly available but can be requested by contacting the corresponding author.

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Acknowledgements

The authors like to express great appreciation to Dr. Bofu Yu of Griffith University, Australia, for providing daily precipitation data of the Cataract Dam and Port Macquarie stations; to Dr. Alfredo Borges de Campos of Universidade Federal de Goias, Brazil, for data of the Campinas station; and to Dr. Donal Mullan of Queen’s University Belfast, UK, for data of the Armagh and Durham stations.

Code availability

The custom code was only used for calculations, and it can be provided by contacting the corresponding author.

Funding

This work was partially supported by the National Natural Science Foundation of China (Grant No. 52079093; 51779176), the Hubei Provincial Natural Science Foundation of China (Grant No. 2020CFA100), and the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) funded by the Ministry of Education and State Administration of Foreign Experts Affairs P.R. China (Grant No. B18037).

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Authors and Affiliations

  1. State Key Laboratory of Water Resources & Hydropower Engineering Science, Wuhan University, 299 Bayi Road, Wuchang Distinct, Wuhan, 430072, Hubei, China

    Jie Chen

  2. USDA-ARS, Grazinglands Research Laboratory, 7207W. Cheyenne St., El Reno, OK, 73036, USA

    Xunchang John Zhang

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  1. Jie Chen
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Contributions

JC and XJZ conceived the original idea, designed the methodology, and collected the data. JC developed the model code and performed the simulations. JC and XJZ contributed to the interpretation of results. JC wrote the paper, and XJZ revised the paper.

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Correspondence to Jie Chen.

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Chen, J., Zhang, X.J. Challenges and potential solutions in statistical downscaling of precipitation. Climatic Change 165, 63 (2021). https://doi.org/10.1007/s10584-021-03083-3

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