Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products

Abstract A consistent approach to evaluate the annual wettest day (Rx1day) across global and regional gridded observational datasets is presented using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) to define climatological regions. Global daily 1° × 1° latitude/longitude gridded products available from the Frequent Rainfall Observations on Grids (FROGS) database are compared with regional high-resolution (∼1–25 km) daily gridded rainfall datasets using several interpolation methods and order of operation. Climatologies are calculated for each global product and region using the overlapping period 2001–16, with global datasets then organized into in situ, satellite, and reanalysis groupings and compared with each other and the regional reference. Our findings show that reanalyses (especially CFSR and MERRA-2) tend to be among the wetter products for precipitation extremes in most regions and that reanalysis groupings also have the largest spread. Perhaps surprisingly, regional datasets are often among the drier, if not the driest products in many regions (especially Southeast Asia, Eurasia, and the Middle East), and are almost always drier than reanalyses except in a few cases. Rx1day is significantly positively correlated in most regions and products except in a handful of cases where data issues are likely to affect correlations. Rx1day timing deviates substantially between products, but agreement is highest among in situ products (40%–70% of the time) especially in data-dense regions with least agreement among reanalyses (10%–40% of the time). Despite uncertainties, the mean relative long-term trend estimates in Rx1day averaged across global land areas, with respect to increases in global mean temperature, are close to 7% °C −1 . Significance Statement While there have been numerous global and regional assessments of trends and variability of historical rainfall extremes, there has been little coordination or limited ability to compare across studies or to use regional products consistently when evaluating global products. Our results show for the first time that despite large uncertainty in the wettest day of the year [annual wettest day (Rx1day)] estimates, regional (continental scale) gridded precipitation products are consistently drier than their global counterparts. However, the trend in Rx1day averaged across all products is broadly consistent with the global increase of ∼7% °C −1 found in other studies. Given the huge spread in observations of daily precipitation extremes, our findings have implications for their efficacy in informing global monitoring, event attribution, and model evaluation efforts.

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Publication Details

Journal
Journal of Climate
Published
2025-10-15
DOI
https://doi.org/10.1175/jcli-d-25-0222.1
Primary Topic
Precipitation Measurement and Analysis
Type
article
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article

Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products

Xuebin Zhang, Lisa V. Alexander, Phuong Loan Nguyen, Margot Bador et al.
Journal of Climate
Precipitation Measurement and Analysis
article

Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products

Xuebin Zhang, Lisa V. Alexander, Phuong Loan Nguyen, Margot Bador, Rémy Roca, Xu Deng, Blair Trewin, Chris Lennard, Markus G. Donat, Andrew King, Simon F. B. Tett, Seung‐Ki Min, Lincoln Muniz Alves, Robert Dunn, Peter B. Gibson
article en

Abstract

Abstract A consistent approach to evaluate the annual wettest day (Rx1day) across global and regional gridded observational datasets is presented using Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) to define climatological regions. Global daily 1° × 1° latitude/longitude gridded products available from the Frequent Rainfall Observations on Grids (FROGS) database are compared with regional high-resolution (∼1–25 km) daily gridded rainfall datasets using several interpolation methods and order of operation. Climatologies are calculated for each global product and region using the overlapping period 2001–16, with global datasets then organized into in situ, satellite, and reanalysis groupings and compared with each other and the regional reference. Our findings show that reanalyses (especially CFSR and MERRA-2) tend to be among the wetter products for precipitation extremes in most regions and that reanalysis groupings also have the largest spread. Perhaps surprisingly, regional datasets are often among the drier, if not the driest products in many regions (especially Southeast Asia, Eurasia, and the Middle East), and are almost always drier than reanalyses except in a few cases. Rx1day is significantly positively correlated in most regions and products except in a handful of cases where data issues are likely to affect correlations. Rx1day timing deviates substantially between products, but agreement is highest among in situ products (40%–70% of the time) especially in data-dense regions with least agreement among reanalyses (10%–40% of the time). Despite uncertainties, the mean relative long-term trend estimates in Rx1day averaged across global land areas, with respect to increases in global mean temperature, are close to 7% °C −1 . Significance Statement While there have been numerous global and regional assessments of trends and variability of historical rainfall extremes, there has been little coordination or limited ability to compare across studies or to use regional products consistently when evaluating global products. Our results show for the first time that despite large uncertainty in the wettest day of the year [annual wettest day (Rx1day)] estimates, regional (continental scale) gridded precipitation products are consistently drier than their global counterparts. However, the trend in Rx1day averaged across all products is broadly consistent with the global increase of ∼7% °C −1 found in other studies. Given the huge spread in observations of daily precipitation extremes, our findings have implications for their efficacy in informing global monitoring, event attribution, and model evaluation efforts.

Journal of ClimateVol. 38(24)
Institució Catalana de Recerca i Estudis Avançats (ES), Pohang University of Science and Technology (KR), Bureau of Meteorology (AU), Met Office (GB), Centre National de la Recherche Scientifique (FR), Université Toulouse III - Paul Sabatier (FR), University of Cape Town (ZA), The University of Melbourne (AU), Université Fédérale de Toulouse Midi-Pyrénées (FR), University of Victoria (CA), Barcelona Supercomputing Center (ES), UNSW Sydney (AU), Pacific Institute for Climate Solutions (CA), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (FR), Laboratoire d’Études en Géophysique et Océanographie Spatiales (FR), ARC Centre of Excellence for Climate System Science (AU), Institut de Recherche pour le Développement (FR), ACT Government (AU), National Institute of Water and Atmospheric Research (NZ), Instituto Nacional de Pesquisas Espaciais (BR), Universitat Politècnica de Catalunya (ES), University of Edinburgh (GB)
Openalex Percentile: Top 66%
Precipitation Measurement and Analysis
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