Title: High resolution spatial-temporal downscaling model for historical daily precipitation using INLA

Authors: Pedro Garrett; Filipe Duarte Santos; Rui Perdigão

Addresses: CE3C – Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal; 2adapt – Climate Adaptation Services LDA, Rua António Sampaio de Souza Maldonado, No. 15, 7100-699 Veiros, Portugal ' CE3C – Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal ' CE3C – Centre for Ecology, Evolution and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal; Meteoceanics Institute for Complex System Science, Vienna, Austria

Abstract: Precipitation and precipitation extremes have long been challenging to estimate. Significant progress has been made with successive generations of Earth system models capable of simulating our climate with a global coverage. In this paper, a new statistical approach is presented based on the integrated nested Laplace approximation (INLA) method to downscale historical daily precipitation rates. The spatial and spatio-temporal structures were used in a Bayesian approach, to produce a daily 5 km regular grid for continental Portugal. Results show the capability of the method to provide fast results aligned with the observations, but still underestimating precipitation events higher than 100 mm/day.

Keywords: statistical downscaling; climate change; precipitation; weather extremes; INLA; spatial-temporal modelling; Bayesian statistics; ERA5; reanalysis; historical climate.

DOI: 10.1504/IJGW.2023.130985

International Journal of Global Warming, 2023 Vol.30 No.2, pp.161 - 173

Received: 30 Jun 2022
Received in revised form: 06 Oct 2022
Accepted: 07 Oct 2022
Published online: 17 May 2023 *

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