A causality-guided statistical approach for modelling extreme mei-yu rainfall based on known large-scale modes – a pilot study

Ng, K. S., Leckebusch, G. C. and Hodges, K. I. ORCID: https://orcid.org/0000-0003-0894-229X (2022) A causality-guided statistical approach for modelling extreme mei-yu rainfall based on known large-scale modes – a pilot study. Advances in Atmospheric Sciences, 39. pp. 1925-1940. ISSN 0256-1530 doi: 10.1007/s00376-022-1348-3

Abstract/Summary

Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall, e.g. MYR. Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, we might be able to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models. In this pilot study, we demonstrate that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with literature. We also show the importance of temporal resolution in constructing statistical models for MYR, which is in good agreement with literature. This shows the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach will be discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/102939
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Springer
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