PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018


  • 1.

    Tapiador, F. J. et al. Global precipitation measurement: Methods, datasets and applications. Atmospheric Research 104–105, 70–97 (2012).

    ADS 

    Google Scholar
     

  • 2.

    Kucera, P. A. et al. Precipitation from space: Advancing Earth system science. Bulletin of the American Meteorological Society 94, 365–375 (2013).

    ADS 

    Google Scholar
     

  • 3.

    Kirschbaum, D. B. et al. NASA’s remotely sensed precipitation: A reservoir for applications users. Bulletin of the American Meteorological Society 98, 1169–1184 (2017).

    ADS 

    Google Scholar
     

  • 4.

    Dai, A. Precipitation characteristics in eighteen coupled climate models. Journal of Climate 19, 4605–4630 (2006).

    ADS 

    Google Scholar
     

  • 5.

    Bosilovich, M. G., Chen, J., Robertson, F. R. & Adler, R. F. Evaluation of global precipitation in reanalyses. Journal of Applied Meteorology and Climatology 47, 2279–2299 (2008).

    ADS 

    Google Scholar
     

  • 6.

    Zhu, Y. & Luo, Y. Precipitation calibration based on the frequency-matching method. Weather and Forecasting 30, 1109–1124 (2015).

    ADS 

    Google Scholar
     

  • 7.

    Xie, P. et al. Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. Journal of Hydrometeorology 18, 1617–1641 (2017).

    ADS 

    Google Scholar
     

  • 8.

    Karbalaee, N., Hsu, K., Sorooshian, S. & Braithwaite, D. Bias adjustment of infrared based rainfall estimation using passive microwave satellite rainfall data. Journal of Geophysical Research: Atmospheres 122, 3859–3876 (2017).

    ADS 

    Google Scholar
     

  • 9.

    Lumbroso, D. M., Boyce, S., Bast, H. & Walmsley, N. The challenges of developing rainfall intensity-duration-frequency curves and national flood hazard maps for the Caribbean. Journal of Flood Risk Management 4, 42–52 (2011).


    Google Scholar
     

  • 10.

    Yan, H. et al. Next-generation intensity-duration-frequency curves to reduce errors in peak flood design. Journal of Hydrologic Engineering 24, 04019020 (2019).


    Google Scholar
     

  • 11.

    Cloke, H. L. & Pappenberger, F. Ensemble flood forecasting: A review. Journal of Hydrology 375, 613–626 (2009).

    ADS 

    Google Scholar
     

  • 12.

    Hirpa, F. A. et al. The effect of reference climatology on global flood forecasting. Journal of Hydrometeorology 17, 1131–1145 (2016).

    ADS 

    Google Scholar
     

  • 13.

    Siegmund, J., Bliefernicht, J., Laux, P. & Kunstmann, H. Toward a seasonal precipitation prediction system for West Africa: Performance of CFSv2 and high-resolution dynamical downscaling. Journal of Geophysical Research: Atmospheres 120, 7316–7339 (2015).

    ADS 

    Google Scholar
     

  • 14.

    Ricko, M., Adler, R. F. & Huffman, G. J. Climatology and interannual variability of quasi-global intense precipitation using satellite observations. Journal of Climate 29, 5447–5468 (2016).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 15.

    Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology 8, 38–55 (2007).

    ADS 

    Google Scholar
     

  • 16.

    Trenberth, K. E. & Zhang, Y. How often does it really rain? Bulletin of the American Meteorological Society 99, 289–298 (2018).

    ADS 

    Google Scholar
     

  • 17.

    Li, X.-F. et al. Global distribution of the intensity and frequency of hourly precipitation and their responses to ENSO. Climate Dynamics 1–17 (2020).

  • 18.

    Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xi, P. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology 5, 487–503 (2004).

    ADS 

    Google Scholar
     

  • 19.

    Stephens, G. L. & Kummerow, C. D. The remote sensing of clouds and precipitation from space: a review. Journal of the Atmospheric Sciences 64, 3742–3765 (2007).

    ADS 

    Google Scholar
     

  • 20.

    Sun, Q. et al. A review of global precipitation datasets: data sources, estimation, and intercomparisons. Reviews of Geophysics 56, 79–107 (2018).

    ADS 

    Google Scholar
     

  • 21.

    Prakash, S. et al. A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. Journal of Hydrology 556, 865–876 (2018).

    ADS 

    Google Scholar
     

  • 22.

    Cao, Q., Painter, T. H., Currier, W. R., Lundquist, J. D. & Lettenmaier, D. P. Estimation of precipitation over the OLYMPEX domain during winter 2015/16. Journal of Hydrometeorology 19, 143–160 (2018).

    ADS 

    Google Scholar
     

  • 23.

    Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrology and Earth System Sciences 23, 207–224 (2019).

    ADS 

    Google Scholar
     

  • 24.

    Kidd, C. et al. Intercomparison of high-resolution precipitation products over northwest Europe. Journal of Hydrometeorology 13, 67–83 (2012).

    ADS 

    Google Scholar
     

  • 25.

    Levizzani, V., Laviola, S. & Cattani, E. Detection and measurement of snowfall from space. Remote Sensing 3, 145–166 (2011).

    ADS 

    Google Scholar
     

  • 26.

    Skofronick-Jackson, G. et al. Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEX): for measurement’s sake, let it snow. Bulletin of the American Meteorological Society 96, 1719–1741 (2015).

    ADS 

    Google Scholar
     

  • 27.

    Courty, L. G., Wilby, R. L., Hillier, J. K. & Slater, L. J. Intensity-duration-frequency curves at the global scale. Environmental Research Letters 14, 084045 (2019).

    ADS 

    Google Scholar
     

  • 28.

    Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society (2020).

  • 29.

    Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 30.

    Stephens, G. L. et al. Dreary state of precipitation in global models. Journal of Geophysical Research: Atmospheres 115 (2010).

  • 31.

    Kang, S. & Ahn, J.-B. Global energy and water balances in the latest reanalyses. Asia-Pacific Journal of Atmospheric Sciences 51, 293–302 (2015).

    ADS 

    Google Scholar
     

  • 32.

    Sun, Y., Solomon, S., Dai, A. & Portmann, R. W. How often does it rain? Journal of Climate 19, 916–934 (2006).

    ADS 

    Google Scholar
     

  • 33.

    Dietzsch, F. et al. A global ETCCDI-based precipitation climatology from satellite and rain gauge measurements. Climate 5 (2017).

  • 34.

    Schamm, K. et al. Global gridded precipitation over land: a description of the new GPCC First Guess Daily product. Earth System Science Data 6, 49–60 (2014).

    ADS 

    Google Scholar
     

  • 35.

    Hirpa, F. A., Gebremichael, M. & Hopson, T. Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. Journal of Applied Meteorology and Climatology 49, 1044–1051 (2010).

    ADS 

    Google Scholar
     

  • 36.

    Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K. & Ribbe, L. Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile. Hydrology and Earth System Sciences 21, 1295–1320 (2017).

    ADS 

    Google Scholar
     

  • 37.

    Beck, H. E. et al. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences 21, 6201–6217 (2017).

    ADS 
    CAS 

    Google Scholar
     

  • 38.

    Beck, H. E. et al. MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrology and Earth System Sciences 21, 589–615 (2017).

    ADS 

    Google Scholar
     

  • 39.

    Kidd, C. et al. So, how much of the Earth’s surface is covered by rain gauges? Bulletin of the American Meteorological Society 98, 69–78 (2017).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Briggs, P. R. & Cogley, J. G. Topographic bias in mesoscale precipitation networks. Journal of Climate 9, 205–218 (1996).

    ADS 

    Google Scholar
     

  • 41.

    Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology 115, 15–40 (2014).

    ADS 

    Google Scholar
     

  • 42.

    Mishra, A. K. & Coulibaly, P. Developments in hydrometric network design: A review. Reviews of Geophysics 47 (2009).

  • 43.

    Ensor, L. A. & Robeson, S. M. Statistical characteristics of daily precipitation: comparisons of gridded and point datasets. Journal of Applied Meteorology and Climatology 47, 2468–2476 (2008).

    ADS 

    Google Scholar
     

  • 44.

    Hofstra, N., Haylock, M., New, M. & Jones, P. D. Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. Journal of Geophysical Research: Atmospheres 114 (2009).

  • 45.

    Huffman, G. J. et al. NASA global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD), NASA/GSFC, Greenbelt, MD 20771, USA (2014).

  • 46.

    Huffman, G. J., Bolvin, D. T. & Nelkin, E. J. Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. Tech. Rep., NASA/GSFC, Greenbelt, MD 20771, USA (2018).

  • 47.

    Olsson, J., Berg, P. & Kawamura, A. Impact of RCM spatial resolution on the reproduction of local, subdaily precipitation. Journal of Hydrometeorology 16, 534–547 (2015).

    ADS 

    Google Scholar
     

  • 48.

    Dai, A. Global precipitation and thunderstorm frequencies. Part I: Seasonal and interannual variations. Journal of Climate 14, 1092–1111 (2001).

    ADS 

    Google Scholar
     

  • 49.

    Qian, J.-H. Why precipitation is mostly concentrated over islands in the Maritime Continent. Journal of the Atmospheric Sciences 65, 1428–1441 (2008).

    ADS 

    Google Scholar
     

  • 50.

    Ogino, S.-Y., Yamanaka, M. D., Mori, S. & Matsumoto, J. How much is the precipitation amount over the tropical coastal region? Journal of Climate 29, 1231–1236 (2016).

    ADS 

    Google Scholar
     

  • 51.

    Curtis, S. Means and long-term trends of global coastal zone precipitation. Scientific Reports 9, 5401 (2019).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 52.

    Beck, H. E. et al. MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bulletin of the American Meteorological Society 100, 473–500 (2019).

    ADS 

    Google Scholar
     

  • 53.

    Schlosser, C. A. & Houser, P. R. Assessing a satellite-era perspective of the global water cycle. Journal of Climate 20, 1316–1338 (2007).

    ADS 

    Google Scholar
     

  • 54.

    Ellis, T. D., L’Ecuyer, T., Haynes, J. M. & Stephens, G. L. How often does it rain over the global oceans? The perspective from CloudSat. Geophysical Research Letters 36 (2009).

  • 55.

    Hardwick Jones, R., Westra, S. & Sharma, A. Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity. Geophysical Research Letters 37 (2010).

  • 56.

    Peleg, N. et al. Intensification of convective rain cells at warmer temperatures observed from high-resolution weather radar data. Journal of Hydrometeorology 19, 715–726 (2018).

    ADS 

    Google Scholar
     

  • 57.

    Allan, R. P. et al. Advances in understanding large-scale responses of the water cycle to climate change. Annals of the New York Academy of Sciences (2020).

  • 58.

    Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W. & Yorty, D. P. Where are the most intense thunderstorms on earth? Bulletin of the American Meteorological Society 87, 1057–1072 (2006).

    ADS 

    Google Scholar
     

  • 59.

    Liu, C. & Zipser, E. J. The global distribution of largest, deepest, and most intense precipitation systems. Geophysical Research Letters 42, 3591–3595 (2015).

    ADS 

    Google Scholar
     

  • 60.

    Behrangi, A., Tian, Y., Lambrigtsen, B. H. & Stephens, G. L. What does CloudSat reveal about global land precipitation detection by other spaceborne sensors? Water Resources Research 50, 4893–4905 (2014).

    ADS 

    Google Scholar
     

  • 61.

    Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E. & Houston, T. G. An overview of the Global Historical Climatology Network-Daily database. Journal of Atmospheric and Oceanic Technology 29, 897–910 (2012).

    ADS 

    Google Scholar
     

  • 62.

    Lewis, E. et al. GSDR: a global sub-daily rainfall dataset. Journal of Climate 32, 4715–4729 (2019).

    ADS 

    Google Scholar
     

  • 63.

    Blenkinsop, S. et al. The INTENSE project: using observations and models to understand the past, present and future of sub-daily rainfall extremes. Advances in Science and Research 15, 117–126 (2018).

    ADS 

    Google Scholar
     

  • 64.

    Goodison, B. E., Louie, P. Y. T. & Yang, D. WMO solid precipitation intercomparison. Tech. Rep. WMO/TD-872, World Meteorological Organization, Geneva (1998).

  • 65.

    Daly, C., Gibson, W. P., Taylor, G. H., Doggett, M. K. & Smith, J. I. Observer bias in daily precipitation measurements at United States cooperative network stations. Bulletin of the American Meteorological Society 88, 899–912 (2007).

    ADS 

    Google Scholar
     

  • 66.

    Sevruk, B., Ondrás, M. & Chvíla, B. The WMO precipitation measurement intercomparisons. Atmospheric Research 92, 376–380 (2009).

    ADS 

    Google Scholar
     

  • 67.

    Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G. & Vose, R. S. Comprehensive automated quality assurance of daily surface observations. Journal of Applied Meteorology and Climatology 49, 1615–1633 (2010).

    ADS 

    Google Scholar
     

  • 68.

    Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data 2, 150066 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 69.

    Meteorological Organization (WMO), W. Guide to hydrological practices, volume II: Management of water resources and applications of hydrological practices, http://www.wmo.int/pages/prog/hwrp/publications/guide/english/168_Vol_II_en.pdf (WMO, Geneva, Switzerland, 2009).

  • 70.

    Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods 17, 261–272 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 71.

    Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. Journal of Geophysical Research: Atmospheres 113 (2008).

  • 72.

    Oliphant, T. E. NumPy: A guide to NumPy. USA: Trelgol Publishing. www.numpy.org (2006).

  • 73.

    van der Walt, S., Colbert, S. C. & Varoquaux, G. The Numpy array: A structure for efficient numerical computation. Computing in Science Engineering 13, 22–30 (2011).


    Google Scholar
     

  • 74.

    Bishop, C. M. Neural networks for pattern recognition. (Clarendon Press, Oxford, UK, 1995).


    Google Scholar
     

  • 75.

    Coulibaly, P., Dibike, Y. B. & Anctil, F. Downscaling precipitation and temperature with temporal neural networks. Journal of Hydrometeorology 6, 483–496 (2005).

    ADS 

    Google Scholar
     

  • 76.

    Kim, J.-W. & Pachepsky, Y. A. Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. Journal of Hydrology 394, 305–314 (2010).

    ADS 

    Google Scholar
     

  • 77.

    Nastos, P., Paliatsos, A., Koukouletsos, K., Larissi, I. & Moustris, K. Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmospheric Research 144, 141–150 (2014).

    ADS 

    Google Scholar
     

  • 78.

    Hutchinson, M. F. Interpolation of rainfall data with thin plate smoothing splines — part I: Two dimensional smoothing of data with short range correlation. Journal of Geographic Information and Decision Analysis 2, 168–185 (1998).


    Google Scholar
     

  • 79.

    Smith, R. B. et al. Orographic precipitation and air mass transformation: An Alpine example. Quarterly Journal of the Royal Meteorological Society 129, 433–454 (2003).

    ADS 

    Google Scholar
     

  • 80.

    Roe, G. H. Orographic precipitation. Annual Review of Earth and Planetary Sciences 33, 645–671 (2005).

    ADS 
    CAS 

    Google Scholar
     

  • 81.

    Molnar, P., Fatichi, S., Gaál, L., Szolgay, J. & Burlando, P. Storm type effects on super Clausius-Clapeyron scaling of intense rainstorm properties with air temperature. Hydrology and Earth System Sciences 19, 1753–1766 (2015).

    ADS 

    Google Scholar
     

  • 82.

    Benestad, R., Nychka, D. & Mearns, L. Spatially and temporally consistent prediction of heavy precipitation from mean values. Nature Climate Change 2, 544–547 (2012).

    ADS 

    Google Scholar
     

  • 83.

    Funk, C. et al. A global satellite assisted precipitation climatology. Earth System Science Data 7, 275–287 (2015).

    ADS 

    Google Scholar
     

  • 84.

    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302–4315 (2017).

    ADS 

    Google Scholar
     

  • 85.

    Halkjær, S. & Winther, O. The effect of correlated input data on the dynamics of learning. In NIPS, 169–175, http://papers.nips.cc/paper/1254-the-effect-of-correlated-input-data-on-the-dynamics-of-learning (1996).

  • 86.

    Overeem, A., Buishand, A. & Holleman, I. Rainfall depth-duration-frequency curves and their uncertainties. Journal of Hydrology 348, 124–134 (2008).

    ADS 

    Google Scholar
     

  • 87.

    Rao, C. R. Linear statistical inference and its applications (2 edn, John Wiley and Sons, New York, 1973).

  • 88.

    Chai, T. & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development 7, 1247–1250 (2014).

    ADS 

    Google Scholar
     

  • 89.

    Willmott, C. J., Robeson, S. M. & Matsuura, K. Climate and other models may be more accurate than reported. Eos 98 (2017).

  • 90.

    Sharifi, E., Steinacker, R. & Saghafian, B. Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: preliminary results. Remote Sensing 8 (2016).

  • 91.

    Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology 370, 80–91 (2009).

    ADS 

    Google Scholar
     

  • 92.

    Beck, H. E. et al. PPDIST: global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018. figshare https://doi.org/10.6084/m9.figshare.12317219 (2020).

  • 93.

    Zolina, O., Kapala, A., Simmer, C. & Gulev, S. K. Analysis of extreme precipitation over Europe from different reanalyses: a comparative assessment. Global and Planetary Change 44, 129–161 (2004).

    ADS 

    Google Scholar
     

  • 94.

    Mehran, A., AghaKouchak, A. & Capabilities, A. of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations. Hydrological Processes 28, 2262–2270 (2014).

    ADS 

    Google Scholar
     

  • 95.

    Herold, N., Behrangi, A. & Alexander, L. V. Large uncertainties in observed daily precipitation extremes over land. Journal of Geophysical Research: Atmospheres 122, 668–681 (2017).

    ADS 

    Google Scholar
     

  • 96.

    Legates, D. R. A climatology of global precipitation. Ph.D. thesis, University of Delaware (1988).

  • 97.

    Herold, N., Alexander, L. V., Donat, M. G., Contractor, S. & Becker, A. How much does it rain over land? Geophysical Research Letters 43, 341–348 (2016).

    ADS 

    Google Scholar
     

  • 98.

    Tustison, B., Harris, D. & Foufoula-Georgiou, E. Scale issues in verification of precipitation forecasts. Journal of Geophysical Research: Atmospheres 106, 11775–11784 (2001).


    Google Scholar
     

  • 99.

    Chen, C.-T. & Knutson, T. On the verification and comparison of extreme rainfall indices from climate models. Journal of Climate 21, 1605–1621 (2008).

    ADS 

    Google Scholar
     

  • 100.

    Harrigan, S. et al. GloFAS-ERA5 operational global river discharge reanalysis 1979–present. Earth System Science Data Discussions 2020, 1–23 (2020).


    Google Scholar
     

  • 101.

    Tian, Y. & Peters-Lidard, C. D. Systematic anomalies over inland water bodies in satellite-based precipitation estimates. Geophysical Research Letters 34 (2007).

  • 102.

    You, Y., Wang, N.-Y., Ferraro, R. & Rudlosky, S. Quantifying the snowfall detection performance of the GPM microwave imager channels over land. Journal of Hydrometeorology 18, 729–751 (2017).

    ADS 

    Google Scholar
     

  • 103.

    Kubota, T. et al. Verification of high-resolution satellite-based rainfall estimates around Japan using a gauge-calibrated ground-radar dataset. Journal of the Meteorological Society of Japan. Ser. II 87A, 203–222 (2009).


    Google Scholar
     

  • 104.

    Tian, Y. et al. Component analysis of errors in satellite-based precipitation estimates. Journal of Geophysical Research: Atmospheres 114 (2009).

  • 105.

    Derin, Y. et al. Evaluation of GPM-era global satellite precipitation products over multiple complex terrain regions. Remote Sensing 11 (2019).

  • 106.

    Donohue, R. J., Roderick, M. L. & McVicar, T. R. Roots, storms and soil pores: Incorporating key ecohydrological processes into Budyko’s hydrological model. Journal of Hydrology 436–437, 35–50 (2012).

    ADS 

    Google Scholar
     

  • 107.

    Liu, Q. et al. The hydrological effects of varying vegetation characteristics in a temperate water-limited basin: Development of the dynamic Budyko-Choudhury-Porporato (dBCP) model. Journal of Hydrology 543, 595–611 (2016).

    ADS 

    Google Scholar
     

  • 108.

    Kuligowski, R. J. An overview of National Weather Service quantitative precipitation estimates. United States, National Weather Service, Techniques Development Laboratory. https://repository.library.noaa.gov/view/noaa/6879 (1997).

  • 109.

    Wolff, D. B. & Fisher, B. L. Comparisons of instantaneous TRMM ground validation and satellite rain-rate estimates at different spatial scales. Journal of Applied Meteorology and Climatology 47, 2215–2237 (2008).

    ADS 

    Google Scholar
     

  • 110.

    Beck, H. E. et al. Bias correction of global precipitation climatologies using discharge observations from 9372 catchments. Journal of Climate 33, 1299–1315 (2020).

    ADS 

    Google Scholar
     

  • 111.

    Osborn, T. J. & Hulme, M. Development of a relationship between station and grid-box rainday frequencies for climate model evaluation. Journal of Climate 10, 1885–1908 (1997).

    ADS 

    Google Scholar
     

  • 112.

    Pietersen, J. P. J., Gericke, O. J., Smithers, J. C. & Woyessa, Y. E. Review of current methods for estimating areal reduction factors applied to South African design point rainfall and preliminary identification of new methods. Journal of the South African Institution of Civil Engineering 57, 16–30 (2015).


    Google Scholar
     

  • 113.

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    MathSciNet 
    MATH 

    Google Scholar
     

  • 114.

    Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. Plos Biology 14, 1–20 (2016).


    Google Scholar
     

  • 115.

    Yamazaki, D. et al. A high-accuracy map of global terrain elevations. Geophysical Research Letters 44, 5844–5853 (2017).

    ADS 

    Google Scholar
     

  • 116.

    Legates, D. R. & Bogart, T. A. Estimating the proportion of monthly precipitation that falls in solid form. Journal of Hydrometeorology 10, 1299–1306 (2009).

    ADS 

    Google Scholar
     



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