A precipitation dataset is created to estimate a reproduction period of several thousand years for stochastic flood risk assessment in the non-life insurance sector. A stochastic precipitation model for natural hazard risk assessment developed in a previous study was applied to a large ensemble data. The model was used to obtain the precipitation ensembles for the recent and future climate by +2 K and +4 K increases in mean temperature, respectively. We successfully created 10,000 years of precipitation data, which makes it possible to obtain precipitation data over a 1,000-year return period.

1. [1] K. Mitchell-Wallace, M. Jones, J. Hillier, and M. Foote, “Natural Catastrophe Risk Management and Modelling: A Practitioner’s Guide,” John Wiley & Sons, 2017.
2. [2] World Meteorological Organization, “WMO Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019),” 2021.
3. [3] T. Loridan, S. Khare, E. Scherer, M. Dixon, and E. Bellone, “Parametric modeling of transitioning cyclone wind fields for risk assessment studies in the Western North Pacific,” J. of Applied Meteorology and Climatology, Vol.54, No.3, pp. 624-642, 2015. https://doi.org/10.1175/JAMC-D-14-0095.1
4. [4] AIR Worldwide, “AIR Hurricane Model for the United States,” 2018. https://www.verisk.com/siteassets/media/downloads/climate/the-air-hurricane-model-for-the-united-states.pdf [Accessed January 30, 2023]
5. [5] R. E. Tuleya, M. DeMaria, and R. J. Kuligowski, “Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms,” Weather and Forecasting, Vol.22, No.1, pp. 56-70, 2007. https://doi.org/10.1175/WAF972.1
6. [6] M. Lonfat, R. Rogers, T. Marchok, and F. D. Marks Jr., “A parametric model for predicting hurricane rainfall,” Monthly Weather Review, Vol.135, No.9, pp. 3086-3097, 2007. https://doi.org/10.1175/MWR3433.1
7. [7] J. Grieser and S. Jewson, “The RMS TC-rain model,” Meteorologische Zeitschrift, Vol.21, No.1, pp. 79-88, 2012. https://doi.org/10.1127/0941-2948/2012/0265
8. [8] D. S. Wilks, “Multisite generalization of a daily stochastic precipitation generation model,” J. of Hydrology, Vol.210, Nos.1-4, pp. 178-191, 1998. https://doi.org/10.1016/S0022-1694(98)00186-3
9. [9] V. T. Chow, D. R. Maidment, and L. W. Mays, “Applied Hydrology,” McGraw-Hill, 1988.
10. [10] S. Coles, “An Introduction to Statistical Modeling of Extreme Values,” Springer, 2001.
11. [11] P. Ailliot, D. Allard, V. Monbet, and P. Naveau, “Stochastic weather generators: An overview of weather type models,” J. de la société française de statistique, Vol.156, No.1, pp. 101-113, 2015.
12. [12] D. S. Wilks, “Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain,” Agricultural and Forest Meteorology, Vol.96, Nos.1-3, pp. 85-101, 1999. https://doi.org/10.1016/S0168-1923(99)00037-4
13. [13] R. Mizuta et al., “Over 5,000 years of ensemble future climate simulations by 60-km global and 20-km regional atmospheric models,” Bulletin of the American Meteorological Society, Vol.98, No.7, pp. 1383-1398, 2017. https://doi.org/10.1175/BAMS-D-16-0099.1
14. [14] M. Ishii and N. Mori, “d4PDF: Large-ensemble and high-resolution climate simulations for global warming risk assessment,” Progress in Earth and Planetary Science, Vol.7, Article No.58, 2020. https://doi.org/10.1186/s40645-020-00367-7
15. [15] Y. Tachikawa et al., “Future change analysis of extreme floods using large ensemble climate simulation data,” J. of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), Vol.73, No.3, pp. 77-90, 2017 (in Japanese). https://doi.org/10.2208/jscejhe.73.77
16. [16] T. J. Yamada, T. Hoshino, and A. Suzuki, “Using a massive high-resolution ensemble climate data set to examine dynamic and thermodynamic aspects of heavy precipitation change,” Atmospheric Science Letters, Vol.22, No.12, e1065, 2021. https://doi.org/10.1002/asl.1065
17. [17] T. Tanaka, K. Kobayashi, and Y. Tachikawa, “Simultaneous flood risk analysis and its future change among all the 109 class-A river basins in Japan using a large ensemble climate simulation database d4PDF,” Environmental Research Letters, Vol.16, No.7, Article No.074059, 2021. https://doi.org/10.1088/1748-9326/abfb2b
18. [18] N. Mori et al., “Future changes in extreme storm surges based on mega-ensemble projection using 60-km resolution atmospheric global circulation model,” Coastal Engineering J., Vol.61, No.3, pp. 295-307, 2019. https://doi.org/10.1080/21664250.2019.1586290
19. [19] N. Mori and T. Shimura, “Tropical cyclone-induced coastal sea level projection and the adaptation to a changing climate,” Cambridge Prisms: Coastal Futures, Vol.1, e4, 2023. https://doi.org/10.1017/cft.2022.6
20. [20] K. Sugawara, M. Inatsu, S. Shimoda, K. Murakami, and T. Hirota, “Risk assessment and possible adaptation of potato production in Hokkaido to climate change using a large number ensemble climate dataset d4PDF,” SOLA, Vol.17, pp. 24-29, 2021. https://doi.org/10.2151/sola.2021-004
21. [21] K. Murakami et al., “Projected changes in field workability of agricultural machinery operations for upland crop production with +4 K warming in Hokkaido, Japan,” J. of Agricultural Meteorology, Vol.78, No.4, pp. 155-163, 2022. https://doi.org/10.2480/agrmet.D-22-00012
22. [22] S. Kawazoe, M. Inatsu, T. J. Yamada, and T. Hoshino, “Climate change impacts on heavy snowfall in Sapporo using 5-km mesh large ensemble simulations,” SOLA, Vol.16, pp. 233-239, 2020. https://doi.org/10.2151/sola.2020-039
23. [23] H. Kawase et al., “Enhancement of heavy daily snowfall in central Japan due to global warming as projected by large ensemble of regional climate simulations,” Climatic Change, Vol.139, No.2, pp. 265-278, 2016. https://doi.org/10.1007/s10584-016-1781-3
24. [24] Y. Kanamori et al., “Global warming effect and adaptation for a flooding event at Motsukisamu River in Sapporo,” SOLA, Vol.18, pp. 249-253, 2022. https://doi.org/10.2151/sola.2022-040
25. [25] M. Inatsu, T. Takemi, and H. Kusaka, “The climate change effect,” Impact, Vol.2020, pp. 20-22, 2020.
26. [26] A. Nishi and H. Kusaka, “Future changes of the extreme high-temperature events influenced by foehn winds in Niigata, Japan,” Atmospheric Science Letters, Vol.24, No.2, e1137, 2022. https://doi.org/10.1002/asl.1137
27. [27] S&P Global Ratings, “Criteria | Insurance | Request for comment: Request for comment: Insurer risk-based capital adequacy – Methodology and assumption.” https://www.spglobal.com/ratings/en/research/articles/230509-criteria-insurance-request-for-comment-request-for-comment-insurer-risk-based-capital-adequacy-methodolo-12693138 [Accessed July 13, 2023]
28. [28] M. Inatsu et al., “Development of a pressure-precipitation transmitter,” J. of Applied Meteorology and Climatology, Vol.58, No.11, pp. 2453-2468, 2019. https://doi.org/10.1175/JAMC-D-19-0070.1
29. [29] S. Kravtsov, N. Tilinina, Y. Zyulyaeva, and S. K. Gulev, “Empirical modeling and stochastic simulation of sea level pressure variability,” J. of Applied Meteorology and Climatology, Vol.55, No.5, pp. 1197-1219, 2016. https://doi.org/10.1175/JAMC-D-15-0186.1
30. [30] Database for Policy Decision Making for Future Climate Change (d4PDF), “Experimental Design of d4PDF.” https://www.miroc-gcm.jp/d4PDF/design_en.html [Accessed January 23, 2023]
31. [31] D. Takabatake and M. Inatsu, “Summertime precipitation in Hokkaido and Kyushu, Japan in response to global warming,” Climate Dynamics, Vol.58, No.5, pp. 1671-1682, 2022. https://doi.org/10.1007/s00382-021-05983-7
32. [32] M. Kawasaki et al., “Technical note on the rate of change in heavy rainfall Intensity for flood control planning to cope with climate change,” Technical Note of National Institute for Land and Infrastructure Management, No.1205, 2022 (in Japanese).
33. [33] S. Watanabe, M. Yamada, S. Abe, and M. Hatono, “Bias correction of d4PDF using a moving window method and their uncertainty analysis in estimation and projection of design rainfall depth,” Hydrological Research Letters, Vol.14, No.3, pp. 117-122, 2020. https://doi.org/10.3178/hrl.14.117
34. [34] S. Watanabe, M. Nakamura, and N. Utsumi, “The development of bias corrected hourly precipitation dataset for AMeDAS stations based on the projections from d4PDF,” J. of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), Vol.74, No.5, pp. I_127-I_132, 2018 (in Japanese). https://doi.org/10.2208/jscejhe.74.5_I_127
35. [35] Japan Meteorological Agency, “General information on climate of Japan.” https://www.data.jma.go.jp/gmd/cpd/longfcst/en/tourist.html [Accessed July 13, 2023]
36. [36] T. Sasai, “Dynamical downscaling data for near future atmospheric projection (from Tohoku to Kyushu) by SI-CAT,” Data Integration and Analysis System (DIAS), 2019. https://doi.org/10.20783/DIAS.562