Alexander et al. (2025) Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products

Identification

• Journal: Journal of Climate
• Year: 2025
• Date: 2025-10-15
• Authors: Lisa V. Alexander, Phuong Loan Nguyen, Markus G. Donat, Robert Dunn, Simon F. B. Tett, Xuebin Zhang, Lincoln Muniz Alves, Margot Bador, Xu Deng, Peter B. Gibson, Andrew King, Chris Lennard, Seung‐Ki Min, Rémy Roca, Blair Trewin
• DOI: 10.1175/jcli-d-25-0222.1

Research Groups

• Climate Change Research Centre, UNSW Sydney, New South Wales, Australia
• ARC Centre of Excellence for 21st Century Weather, UNSW Sydney, New South Wales, Australia
• Barcelona Supercomputing Center, Barcelona, Spain
• Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
• Met Office Hadley Centre, Exeter, UK
• School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
• Pacific Climate Impacts Consortium, University of Victoria, Victoria, B.C., Canada
• National Institute for Space Research (INPE), São José dos Campos, São Paulo, Brazil
• CECI Université de Toulouse, CERFACS/CNRS, Toulouse, France
• ARC Centre of Excellence for Climate Extremes, University of New South Wales, Canberra, ACT, Australia
• National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
• School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Parkville, Victoria, Australia
• Climate System Analysis Group, University of Cape Town, South Africa
• Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea
• Université de Toulouse, Laboratoire d’Etudes en Géophysique et Océanographie Spatiales (CNRS/CNES/IRD/UPS), Toulouse, France
• Bureau of Meteorology, Melbourne, Victoria, Australia

Short Summary

This study consistently evaluates the annual wettest day (Rx1day) across global and regional gridded observational precipitation datasets, revealing that regional products are consistently drier than their global counterparts, yet the long-term trend in Rx1day aligns with the expected 7%/°C increase per global mean temperature rise.

Objective

• To consistently evaluate the annual wettest day (Rx1day) within and across IPCC AR6 climatological regions using global and regional gridded observational datasets, comparing their climatologies, timeseries, trends, spatial correlations, and timing of Rx1day.

Study Configuration

• Spatial Scale: Global land areas, divided into 44 IPCC AR6 regions. Global gridded products are at 1° × 1° latitude/longitude resolution. Regional high-resolution datasets range from approximately 1 km to 25 km.
• Temporal Scale: Common overlapping period of 2001-2016 (2001-2015 for Asia). Trends in Rx1day are calculated over the 32-year period 1985-2016 for datasets with sufficient data.

Methodology and Data

• Models used: Not applicable (observational data comparison).
• Data sources:
  • Global Gridded Products: 14 datasets from the Frequent Rainfall on Grids (FROGS) database (https://frogs.ipsl.fr/), categorized as in situ (CPCv1.0, GPCCFDD2022, REGENALL2019), satellite (CHIRPSv2.0, CMORPHv1.0CRT, GIRAFEv1, GPCPCDRv3.2, GSMAP-gauges-NRT-v8.0, IMERG-v07B-FC, PERSIANNv1_r1), and reanalyses (CFSR, ERA5, JRA-55, MERRA2).
  • Regional Reference Products: High-resolution datasets including Daymet V4 (North America), E-OBS (Europe), APHRODITE (Asia), AGCD (Australia), VCSN (New Zealand), CoSch (South America, in situ/satellite blend), and TAMSAT V3 (Africa, satellite/gauge blend).
  • Temperature Data for Trend Analysis: Berkeley Earth Surface Temperatures (BEST), GISS Surface Temperature Analysis version 4 (GISTEMP v4), and NOAA Global Surface Temperature version 5 (NOAAGlobalTemp v5).
  • Interpolation Methods: Bilinear and first-order conservative remapping for spatial consistency, applied either to daily data before Rx1day calculation or to Rx1day values after calculation.
  • Extreme Index: Annual wettest day (Rx1day) calculated using climpact software.
  • Statistical Analysis: Climatological maps, timeseries, spatial correlation (Pearson’s method with Student t-test for significance), and linear regression of Rx1day trends against global mean surface temperature (GMST).

Main Results

• Reanalysis products, particularly CFSR and MERRA2, consistently show more intense precipitation extremes (Rx1day) and the largest spread across most regions.
• Regional high-resolution datasets are often among the drier, if not the driest, products in many regions (e.g., Southeast Asia, Eurasia, Middle East), and are almost always drier than reanalyses, with New Zealand (VCSN) being a notable exception.
• Rx1day values are significantly positively correlated between most global products and regional references in the majority of AR6 regions (321 out of 465 possible combinations).
• Agreement on the timing (month) of Rx1day is highest among global in situ products (40%-70%) and lowest among reanalyses (10%-40%), especially in data-dense regions.
• The mean relative long-term trend in Rx1day, averaged across global land areas with respect to increases in global mean temperature, is close to 7%/°C, consistent with Clausius-Clapeyron scaling and previous studies, despite large inter-product uncertainties.

Contributions

• This study provides the first systematic intercomparison demonstrating that regional (continental-scale) gridded precipitation products consistently show less intense annual daily precipitation maxima (Rx1day) compared to the majority of global gridded products.
• It highlights significant uncertainties and biases in current gridded precipitation datasets for extreme events, particularly the overestimation by reanalyses and the generally drier nature of regional products for Rx1day.
• The research confirms that the average scaling of Rx1day with global mean surface temperature across global land areas is approximately 7%/°C, aligning with theoretical expectations and previous in situ-based studies, despite the observed inter-product discrepancies.
• The findings have critical implications for the efficacy of precipitation products in global monitoring, extreme event attribution, and climate model evaluation efforts, emphasizing the need for improved, consistent, and transparent observational products.

Funding

• Australian Research Council (ARC) grant FT210100459
• Australian Research Council (ARC) grant CE230100012
• Met Office Hadley Centre Climate Programme (funded by DSIT)
• Horizon Europe project EXPECT (Grant no. 101137656)
• São Paulo Research Foundation (FAPESP, grant 2022/08622-0)
• PrInt/CAPES/INPE (PII- INPE, grant 88881.310543/2018-01)
• Marsden Fast-Start grant (MFP-NIW2302)
• Australian Government’s National Environmental Science Program Climate Systems Hub
• Korea Meteorological Administration Research and Development Program (Grant RS-2024-00403386)

Citation

@article{Alexander2025Less,
  author = {Alexander, Lisa V. and Nguyen, Phuong Loan and Donat, Markus G. and Dunn, Robert and Tett, Simon F. B. and Zhang, Xuebin and Alves, Lincoln Muniz and Bador, Margot and Deng, Xu and Gibson, Peter B. and King, Andrew and Lennard, Chris and Min, Seung‐Ki and Roca, Rémy and Trewin, Blair},
  title = {Less Intense Daily Precipitation Maxima in Regional Compared to Global Gridded Products},
  journal = {Journal of Climate},
  year = {2025},
  doi = {10.1175/jcli-d-25-0222.1},
  url = {https://doi.org/10.1175/jcli-d-25-0222.1}
}