English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Observational evidence of precipitation extremes is vital to better understand how these events might change in a future warmer climate. Over the terrestrial regions of a quasi-global domain, we assess the representation of annual maxima of daily precipitation (Rx1day) in 22 observational products gridded at 1° × 1° resolution and clustered into four categories: station-based  in situ , satellite observations with or without a correction to rain gauges, and reanalyses (5, 8, 4 and 5 datasets, respectively). We also evaluate the interproduct spread across the ensemble and within the four clusters, as a measure of observational uncertainty. We find that reanalyses present a heterogeneous representation of Rx1day in particular over the tropics, and their interproduct spread is the highest compared to any other cluster. Extreme precipitation in satellite data broadly compares well with  in situ -based data. We find a general better agreement with  in situ -based observations and less interproduct spread for the satellite products with a correction to rain gauges compared to the uncorrected products. Given the level of uncertainties associated with the estimation of Rx1day in the observations, none of the datasets can be thought of as the best estimate. Our recommendation is to avoid using reanalyses as observational evidence and to consider  in situ  and satellite data (the corrected version preferably) in an ensemble of products for a better estimation of precipitation extremes and their observational uncertainties. Based on this we choose a subsample of 10 datasets to reduce the interproduct spread in both the representation of Rx1day and its timing throughout the year, compared to all 22 datasets. We emphasize that the recommendations and selection of datasets given here may not be relevant for different precipitation indices, and other grid resolutions and time scales.

Diverse estimates of annual maxima daily precipitation in 22 state-of-the-art quasi-global land observation datasets