{"id":850993,"date":"2026-03-26T06:24:17","date_gmt":"2026-03-26T06:24:17","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/850993\/"},"modified":"2026-03-26T06:24:17","modified_gmt":"2026-03-26T06:24:17","slug":"precipitation-observing-network-gaps-limit-climate-change-impact-assessment","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/850993\/","title":{"rendered":"Precipitation observing network gaps limit climate change impact assessment"},"content":{"rendered":"
  • \n

    Stokstad, E. Scarcity of rain, stream gages threatens forecasts. Science 285<\/b>, 1199\u20131200 (1999).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kidd, C. et al. So, how much of the Earth\u2019s surface is covered by rain gauges? Bull. Am. Meteorol. Soc. 98<\/b>, 69\u201378 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Milly, P. C.D., Dunne, K. A. & Vecchia, A. V. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438<\/b>, 347\u2013350 (2005).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    McDermid, S. et al. Irrigation in the earth system. Nat. Rev. Earth Environ. 4<\/b>, 435\u2013453 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    V\u00f6r\u00f6smarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467<\/b>, 555\u2013561 (2010).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Syed, T. H., Famiglietti, J. S., Chambers, D. P., Willis, J. K. & Hilburn, K. Satellite-based global-ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge. Proc. Natl Acad. Sci. USA 107<\/b>, 17916\u201317921 (2010).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ham, Y.-G. et al. Anthropogenic fingerprints in daily precipitation revealed by deep learning. Nature 622<\/b>, 301\u2013307 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huang, Z., Tan, X., Gan, T. Y., Liu, B. & Chen, X. Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols. Nat. Water 1<\/b>, 614\u2013625 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Tan, X. et al. Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions. Nat. Commun. 14<\/b>, 2796 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7<\/b>, 17966 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, C. et al. Changes in annual extremes of daily temperature and precipitation in CMIP6 models. J. Clim. 34<\/b>, 3441\u20133460 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Thackeray, C. W., Hall, A., Norris, J. & Chen, D. Constraining the increased frequency of global precipitation extremes under warming. Nat. Clim. Change\u00a012<\/b>, 441\u2013448 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    World Meteorological Organization. State of the Global Climate 2021 (WMO, 2022).<\/p>\n<\/li>\n

  • \n

    Barichivich, J. et al. Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4<\/b>, eaat8785 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huang, X., Swain, D. L. & Hall, A. D. Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California. Sci. Adv. 6<\/b>, eaba1323 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Change\u00a010<\/b>, 191\u2013199 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yin, J. & Slater, L. J. E. Increase in drought\u2013heatwave events worsens socio-economic productivity and carbon uptake. Nat. Sustain. 6<\/b>, 241\u2013242 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yin, J. et al. Future socio-ecosystem productivity threatened by compound drought\u2013heatwave events. Nat. Sustain. 6<\/b>, 259\u2013272 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Moazami, S., Golian, S., Kavianpour, M. R. & Hong, Y. Uncertainty analysis of bias from satellite rainfall estimates using copula method. Atmos. Res. 137<\/b>, 145\u2013166 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yang, Q., Dai, Q., Han, D., Zhu, Z. & Zhang, S. Uncertainty analysis of radar rainfall estimates induced by atmospheric conditions using long short-term memory networks. J. Hydrol. 590<\/b>, 125482 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Muto, Y. & Kotsuki, S. Estimating global precipitation fields from rain gauge observations using local ensemble data assimilation. Hydrol. Earth Syst. Sci. 28<\/b>, 5401\u20135417 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, S. et al. Multivariate interpolation and information entropy for optimizing raingauge network in the Mekong River Basin. Hydrol. Sci. J. 64<\/b>, 1439\u20131452 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yang, Z. et al. Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile. J. Geophys. Res. Atmos. 121<\/b>, 3790\u20133806 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23<\/b>, 207\u2013224 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Habib, E., Haile, A. T., Tian, Y. & Joyce, R. J. Evaluation of the high-resolution CMORPH satellite rainfall product using dense rain gauge observations and radar-based estimates. J. Hydrometeorol. 13<\/b>, 1784\u20131798 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ghajarnia, N., Kalantari, Z. & Destouni, G. Data-driven worldwide quantification of large-scale hydroclimatic covariation patterns and comparison with reanalysis and Earth system modeling. Water Resour. Res. 57<\/b>, e2020WR029377 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sun, Q. et al. A Review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56<\/b>, 79\u2013107 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Villarini, G., Mandapaka, P. V., Krajewski, W. F. & Moore, R. J. Rainfall and sampling uncertainties: a rain gauge perspective. J. Geophys. Res. Atmos. 113<\/b>, D11102 (2008).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sadeghi, M. et al. PERSIANN-CNN: precipitation estimation from remotely sensed information using artificial neural networks\u2013convolutional neural networks. J. Hydrometeorol. 20<\/b>, 2273\u20132289 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lanza, L. G. & Stagi, L. Certified accuracy of rainfall data as a standard requirement in scientific investigations. Adv. Geosci. 16<\/b>, 43\u201348 (2008).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xie, P. & Arkin, P. A. Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. J. Clim. 9<\/b>, 840\u2013858 (1996).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huffman, G. J. et al. The new version 3.2 global precipitation climatology project (GPCP) monthly and daily precipitation products. J. Clim. 36<\/b>, 7635\u20137655 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hannah, D. M. et al. Large-scale river flow archives: importance, current status and future needs. Hydrol. Process. 25<\/b>, 1191\u20131200 (2011).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zandler, H., Haag, I. & Samimi, C. Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Sci. Rep. 9<\/b>, 15118 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Riggs, R. M. et al. Extending global river gauge records using satellite observations. Environ. Res. Lett. 18<\/b>, 064027 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lenderink, G. & van Meijgaard, E. Unexpected rise in extreme precipitation caused by a shift in rain type? Nat. Geosci. 2<\/b>, 373 (2009).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    King, A. D., Reid, K. J. & Saunders, K. R. Communicating the link between climate change and extreme rain events. Nat. Geosci. 16<\/b>, 552\u2013554 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, Y. et al. Skilful nowcasting of extreme precipitation with NowcastNet. Nature 619<\/b>, 526\u2013532 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhou, S. et al. Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture\u2013atmosphere feedbacks. Nat. Commun. 13<\/b>, 5756 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Orth, R. & Destouni, G. Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nat. Commun. 9<\/b>, 3602 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, Q. et al. Water limitation regulates positive feedback of increased ecosystem respiration. Nat. Ecol. Evol. 8<\/b>, 1870\u20131876 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Panagos, P. et al. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Sci. Rep. 7<\/b>, 4175 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, D. et al. Exceptional increases in fluvial sediment fluxes in a warmer and wetter High Mountain Asia. Science 374<\/b>, 599\u2013603 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Althoff, D. & Destouni, G. Global patterns in water flux partitioning: irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff. One Earth 6<\/b>, 1246\u20131257 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhou, K. et al. Urban flood risk management needs nature-based solutions: a coupled social-ecological system perspective. npj Urban Sustain. 4<\/b>, 25 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571<\/b>, 257\u2013260 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Destouni, G., Jaramillo, F. & Prieto, C. Hydroclimatic shifts driven by human water use for food and energy production. Nat. Clim. Change\u00a03<\/b>, 213\u2013217 (2013).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Senande-Rivera, M., Insua-Costa, D. & Miguez-Macho, G. Spatial and temporal expansion of global wildland fire activity in response to climate change. Nat. Commun. 13<\/b>, 1208 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, W. et al. Recent decrease of the impact of tropical temperature on the carbon cycle linked to increased precipitation. Nat. Commun. 14<\/b>, 965 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gatti, L. V. et al. Increased Amazon carbon emissions mainly from decline in law enforcement. Nature 621<\/b>, 318\u2013323 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Brandsma, T. Comparison of Automatic and Manual Precipitation Networks in the Netherlands. Report No. TR-347 (Royal Netherlands Meteorological Institute, 2014).<\/p>\n<\/li>\n

  • \n

    Overeem, A. Climatology of Extreme Rainfall from Rain Gauges and Weather Radar. PhD thesis, Wageningen Univ. (2009).<\/p>\n<\/li>\n

  • \n

    China focus: China forges ahead with meteorological modernization. China Daily https:\/\/global.chinadaily.com.cn\/a\/202303\/23\/WS641c1327a31057c47ebb62fb.html<\/a> (2023).<\/p>\n<\/li>\n

  • \n

    Rodda, J. C. Guide to hydrological practices. Hydrol. Sci. J. 56<\/b>, 196\u2013197 (2011).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    World Meteorology Organization in International Meteorological Vocabulary 182\u2013784 (WMO, 1992).<\/p>\n<\/li>\n

  • \n

    Bertini, C. et al. An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data. Hydrol. Res. 52<\/b>, 620\u2013635 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bring, A. & Destouni, G. Arctic climate and water change: model and observation relevance for assessment and adaptation. Surv. Geophys. 35<\/b>, 853\u2013877 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Vousdoukas, M. I. et al. Small island developing states under threat by rising seas even in a 1.5\u2009\u00b0C warming world. Nat. Sustain. 6<\/b>, 1552\u20131564 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Pathirana, A. Small islands: living laboratories revealing global climate and sustainable development challenges. Front. Clim. 6<\/b>, 2024 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mildenberger, M. et al. How publics in small-island states view climate change and international responses to it. Proc. Natl Acad. Sci. USA 122<\/b>, e2415324122 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hemmati, M., Kornhuber, K. & Kruczkiewicz, A. Enhanced urban adaptation efforts needed to counter rising extreme rainfall risks. npj Urban Sustain. 2<\/b>, 16 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rodr\u00edguez-Fonseca, B. et al. Variability and predictability of West African droughts: a review on the role of sea surface temperature anomalies. J. Clim. 28<\/b>, 4034\u20134060 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mohtadi, M., Prange, M. & Steinke, S. Palaeoclimatic insights into forcing and response of monsoon rainfall. Nature 533<\/b>, 191\u2013199 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mulitza, S. et al. Synchronous and proportional deglacial changes in Atlantic meridional overturning and northeast Brazilian precipitation. Paleoceanogr. Paleoclimatol. 32<\/b>, 622\u2013633 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change\u00a07<\/b>, 263\u2013267 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    McCrystall, M. R., Stroeve, J., Serreze, M., Forbes, B. C. & Screen, J. A. New climate models reveal faster and larger increases in arctic precipitation than previously projected. Nat. Commun. 12<\/b>, 6765 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Liu, Q., Bader, J., Jungclaus, J. H. & Matei, D. More extreme summertime North Atlantic oscillation under climate change. Commun. Earth Environ. 6<\/b>, 474 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Serreze, M. C., Barrett, A. P. & Lo, F. Northern high-latitude precipitation as depicted by atmospheric reanalyses and satellite retrievals. Mon. Weather Rev. 133<\/b>, 3407\u20133430 (2005).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rawlins, M. A. & Karmalkar, A. V. Regime shifts in arctic terrestrial hydrology manifested from impacts of climate warming. Cryosphere 18<\/b>, 1033\u20131052 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bring, A. et al. Arctic terrestrial hydrology: a synthesis of processes, regional effects, and research challenges. J. Geophys. Res. Biogeosci. 121<\/b>, 621\u2013649 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    van der Schrier, G., Overeem, A. & Verver, G. Improving Access to Sub-Daily Rain Gauge Data Citation https:\/\/insitu.copernicus.eu\/resources\/library\/improving-access-to-sub-daily-rain-gauge-data-may-2021<\/a> (Copernicus, 2021).<\/p>\n<\/li>\n

  • \n

    Lin, J. et al. Making China\u2019s water data accessible, usable and shareable. Nat. Water 1<\/b>, 328\u2013335 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gu, L. et al. Large anomalies in future extreme precipitation sensitivity driven by atmospheric dynamics. Nat. Commun. 14<\/b>, 3197 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Le, P. V. V. et al. Climate-driven changes in the predictability of seasonal precipitation. Nat. Commun. 14<\/b>, 3822 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Masih, I., Maskey, S., Muss\u00e1, F. E. F. & Trambauer, P. A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol. Earth Syst. Sci. 18<\/b>, 3635\u20133649 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Huang, S. et al. Widespread global exacerbation of extreme drought induced by urbanization. Nat. Cities 1<\/b>, 597\u2013609 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rentschler, J. et al. Global evidence of rapid urban growth in flood zones since 1985. Nature 622<\/b>, 87\u201392 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Liu, S. et al. More heavy precipitation in world urban regions captured through a two-way subgrid land-atmosphere coupling framework in the NCAR CESM2. Geophys. Res. Lett. 51<\/b>, e2024GL108747 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Su, J., Miao, C., Hu, J., Wu, Y. & Ji, J. Widening urban\u2013rural precipitation differences in China: regionally varied intensification since 2000. Earths Future 13<\/b>, e2025EF006657 (2025).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, Q. et al. Oceanic climate changes threaten the sustainability of Asia\u2019s water tower. Nature 615<\/b>, 87\u201393 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, X., Meng, X. & Long, Y. Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Sci. Data 9<\/b>, 563 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, T. & Sun, F. Global gridded GDP data set consistent with the shared socioeconomic pathways. Sci. Data 9<\/b>, 221 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sayre, R. et al. A New Map of Global Ecological Land Units\u2014An Ecophysiographic Stratification Approach 46 (Association of American Geographers, 2014).<\/p>\n<\/li>\n

  • \n

    Wessel, P. & Smith, W. H. F. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. Solid Earth 101<\/b>, 8741\u20138743 (1996).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sayre, R. et al. A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. J. Oper. Oceanogr. 12<\/b>, S47\u2013S56 (2019).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27<\/b>, 379\u2013423 (1948).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     MathSciNet<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Fahle, M., Hohenbrink, T. L., Dietrich, O. & Lischeid, G. Temporal variability of the optimal monitoring setup assessed using information theory. Water Resour. Res. 51<\/b>, 7723\u20137743 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, C., Singh, V. P. & Mishra, A. K. Entropy theory-based criterion for hydrometric network evaluation and design: maximum information minimum redundancy. Water Resour. Res. 48<\/b>, W05521 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ruddell, B. L. & Kumar, P. Ecohydrologic process networks: 1. Identification. Water Resour. Res. 45<\/b>, W03419 (2009).<\/p>\n

    ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Bessenbacher, V., Gudmundsson, L. & Seneviratne, S. I. Optimizing soil moisture station networks for future climates. Geophys. Res. Lett. 50<\/b>, e2022GL101667 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Breiman, L. Random forests. Mach. Learn. 45<\/b>, 5\u201332 (2001).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Pendergrass, A. G. & Hartmann, D. L. Global-mean precipitation and black carbon in AR4 simulations. Geophys. Res. Lett. 39<\/b>, L01703 (2012).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Thorpe, L. & Andrews, T. The physical drivers of historical and 21st century global precipitation changes. Environ. Res. Lett. 9<\/b>, 064024 (2014).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Richardson, T. B. et al. Drivers of precipitation change: an energetic understanding. J. Clim. 31<\/b>, 9641\u20139657 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sand, M., Samset, B. H., Tsigaridis, K., Bauer, S. E. & Myhre, G. Black carbon and precipitation: an energetics perspective. J. Geophys. Res. Atmos. 125<\/b>, e2019JD032239 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Clemens, S. C. et al. Remote and local drivers of Pleistocene South Asian summer monsoon precipitation: a test for future predictions. Sci. Adv. 7<\/b>, eabg3848 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Myhre, G. et al. Scientific data from precipitation driver response model intercomparison project. Sci. Data. 9<\/b>, 123 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn (Springer, 2009).<\/p>\n<\/li>\n

  • \n

    Wu, R. M. X. & Wang, Y. Which objective weight method is better: PCA or entropy? Sci. J. Res. Rev. 3<\/b>, SJRR.MS.ID.000558 (2022).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Su, J. J. Precipitation observing network gaps limit climate change impact assessment (R script). Zenodo https:\/\/doi.org\/10.5281\/zenodo.18364510<\/a> (2026).<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Stokstad, E. Scarcity of rain, stream gages threatens forecasts. Science 285, 1199\u20131200 (1999). Article\u00a0 CAS\u00a0 Google Scholar\u00a0 Kidd,…\n","protected":false},"author":2,"featured_media":850994,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[3843],"tags":[2311,728,3965,9951,3966,70,16,15],"class_list":["post-850993","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-climate-change","tag-environment","tag-humanities-and-social-sciences","tag-hydrology","tag-multidisciplinary","tag-science","tag-uk","tag-united-kingdom"],"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/116294053115804703","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/850993","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=850993"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/850993\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/850994"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=850993"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=850993"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=850993"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}