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An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations

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Abstract

Four statistical downscaling methods, that is, three quantile mapping based techniques including bias-correction and spatial downscaling (BCSD), bias-correction and climate imprint (BCCI), and bias correction constructed analogues with quantile mapping reordering (BCCAQ), and the cumulative distribution function transform (CDF-t) method, are evaluated with daily observed precipitation and surface temperature for 1961–2005 over China. The four downscaling methods improve the accuracy over the driving general climate models (GCMs) significantly in terms of spatial variability, bias, seasonal cycle, and probability density functions of daily series and extreme events. Overall, BCSD outperforms other methods in frequency distributions of daily temperature, precipitation, and extreme precipitation indices such as wet and dry spell lengths. But it comparably has larger biases in temperature-related extremes. When downscaling the seasonal and extreme precipitation, the three quantile mapping based techniques exhibit better capacity than CDF-t in terms of the spatial correlation and bias over all subregions. Whereas CDF-t methods overestimate consecutive wet days and extreme wet indices significantly, as it displays limited improvement over the driving GCMs by producing too many drizzle days using either absolute or relative threshold. All methods are equally skillful in downscaling monthly and seasonal temperature, and the temperature extremes are better reproduced by BCCI, BCCAQ and CDF-t. However, the statistical downscaling methods show limited capacity in improving the interannual variability of temperature and precipitation extremes.

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References

  • Abatzoglou JT, Brown TJ (2012) A comparison of statistical downscaling methods suited for wildfire applications. Int J Climatol 32(5):772–780

    Article  Google Scholar 

  • Bao J, Feng J, Wang Y (2015) Dynamical downscaling simulation and future projection of precipitation over China. J Geophys Res Atmos 120(16):8227–8243

    Article  Google Scholar 

  • Bürger G, Murdock TQ, Werner AT, Sobie SR, Cannon AJ (2012) Downscaling extremes—an intercomparison of multiple statistical methods for present climate. J Clim 25(12):4366–4388

    Article  Google Scholar 

  • Bürger G, Sobie SR, Cannon AJ, Werner AT, Murdock TQ (2013) Downscaling extremes: an intercomparison of multiple methods for future climate. J Clim 26(10):3429–3449

    Article  Google Scholar 

  • Chen W, Jiang Z, Li L (2011) Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. J Clim 24(17):4741–4756

    Article  Google Scholar 

  • Chen H, Xu CY, Guo S (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol 434:36–45

    Article  Google Scholar 

  • Chong-Hai XU, Ying X (2012) The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble. Atmos Ocean Sci Lett 5(6):527–533

    Article  Google Scholar 

  • Chu JT, Xia J, Xu CY, Singh VP (2010) Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theor Appl Climatol 99(1–2):149–161

    Article  Google Scholar 

  • Dixon KW, Lanzante JR, Nath MJ, Hayhoe K, Stoner A, Radhakrishnan A et al (2016) Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results? Clim Change 135(3–4):395–408

    Article  Google Scholar 

  • Eum HI, Cannon AJ, Murdock TQ (2017) Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea. Stoch Environ Res Risk Assess 31(3):683–703

    Article  Google Scholar 

  • Fang GH, Qi HS, Wen X, Zhou L (2016) Analysis of spatiotemporal evolution of extreme monthly precipitation in the nine major basins of China in 21st century under climate change (in Chinese). J Nat Disasters 25(2):15–25

    Google Scholar 

  • Flaounas E, Drobinski P, Vrac M, Bastin S, Lebeaupin-Brossier C, Stéfanon M et al (2013) Precipitation and temperature space-time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods. Clim Dyn 40(11–12):2687–2705

    Article  Google Scholar 

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578

    Article  Google Scholar 

  • Gao XJ, Wang ML, Giorgi F (2013) Climate change over China in the 21st century as simulated by BCC_CSM1.1-RegCM4.0. Atmos Ocean Sci Lett 6:381–386

    Article  Google Scholar 

  • Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. Bull World Meteorol Organ 58(3):175–183

    Google Scholar 

  • Gutiérrez JM, Maraun D, Widmann M, Huth R, Hertig E, Benestad R et al (2018) An intercomparison of a large ensemble of statistical downscaling methods over Europe: results from the VALUE perfect predictor cross-validation experiment. Int J Climatol. https://doi.org/10.1002/joc.5462

    Article  Google Scholar 

  • Gutmann E, Pruitt T, Clark MP, Brekke L, Arnold JR, Raff DA, Rasmussen RM (2014) An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour Res 50(9):7167–7186

    Article  Google Scholar 

  • Hanson RT, Flint LE, Flint AL, Dettinger MD, Faunt CC, Cayan D, Schmid W (2012) A method for physically based model analysis of conjunctive use in response to potential climate changes. Water Resour Res 48(6):W00L08

    Article  Google Scholar 

  • Hertig E, Jacobeit J (2013) A novel approach to statistical downscaling considering nonstationarities: application to daily precipitation in the Mediterranean area. J Geophys Res Atmos 118(2):520–533

    Article  Google Scholar 

  • Hertig E, Maraun D, Bartholy J, Pongracz R, Vrac M, Mares I et al (2018) Comparison of statistical downscaling methods with respect to extreme events over Europe: Validation results from the perfect predictor experiment of the COST Action VALUE. Int J Climatol. https://doi.org/10.1002/joc.5469

    Article  Google Scholar 

  • Hidalgo HG, Dettinger MD, Cayan DR (2008) Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States. California Energy Commission PIER Final Project Report CEC-500-2007-123

  • Hu Y, Maskey S, Uhlenbrook S (2013) Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods. Theor Appl Climatol 112(3–4):447–460

    Article  Google Scholar 

  • Huang R, Chen J, Huang G (2007) Characteristics and variations of the East Asian monsoon system and its impacts on climate disasters in China. Adv Atmos Sci 24(6):993–1023

    Article  Google Scholar 

  • Huang J, Zhang J, Zhang Z, Xu C, Wang B, Yao J (2011) Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method. Stoch Environ Res Risk Assess 25(6):781–792

    Article  Google Scholar 

  • Hunter RD, Meentemeyer RK (2005) Climatologically aided mapping of daily precipitation and temperature. J Appl Meteorol 44(10):1501–1510

    Article  Google Scholar 

  • Hwang S, Graham WD (2013) Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation. Hydrol Earth Syst Sci 17(11):4481–4502

    Article  Google Scholar 

  • Iizumi T, Nishimori M, Dairaku K, Adachi SA, Yokozawa M (2011) Evaluation and intercomparison of downscaled daily precipitation indices over Japan in present-day climate: strengths and weaknesses of dynamical and bias correction-type statistical downscaling methods. J Geophys Res Atmos 116:D1

    Article  Google Scholar 

  • Jakob Themeßl M, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31(10):1530–1544

    Article  Google Scholar 

  • Jiang DB, Wang HJ, Lang XM (2004) Multimodel ensemble prediction for climate change trend of China under SRES A2 scenario. Chin J Geophys 47(5):878–886

    Article  Google Scholar 

  • Lavaysse C, Vrac M, Drobinski P, Lengaigne M, Vischel T (2012) Statistical downscaling of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario. Nat Hazards Earth Syst Sci 12(3):651–670

    Article  Google Scholar 

  • Li B, Zhou TJ (2010) Projected climate change over China under SRES A1B scenario: multi-model ensemble and uncertainties. Adv Clim Change Res 6(4):270–276

    Google Scholar 

  • Liu Z, Xu Z, Charles SP, Fu G, Liu L (2011) Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. Int J Climatol 31(13):2006–2020

    Article  Google Scholar 

  • Liu CM, Liu WB, Fu GB, Ouyang RL (2012) A discussion of some aspects of statistical downscaling in climate impacts assessment (in Chinese). Adv water Sci 23(3):427–437

    Google Scholar 

  • Liu W, Fu G, Liu C, Charles SP (2013) A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain. Theor Appl Climatol 111(3–4):585–600

    Article  Google Scholar 

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M et al (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3):RG3003

    Article  Google Scholar 

  • Maraun D, Huth R, Gutiérrez JM, Martín DS, Dubrovsky M, Fischer A et al (2017) The VALUE perfect predictor experiment: evaluation of temporal variability. Int J Climatol 1:1. https://doi.org/10.1002/joc.5222

    Article  Google Scholar 

  • Maurer EP, Hidalgo HG (2008) Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrol Earth Syst Sci Discuss 4(5):3413–3440

    Article  Google Scholar 

  • Maurer EP, Hidalgo HG, Das T, Dettinger MD, Cayan DR (2010) The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol Earth Syst Sci 14:1125–1138

    Article  Google Scholar 

  • Michelangeli PA, Vrac M, Loukos H (2009) Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys Res Lett 36(11):L11708

    Article  Google Scholar 

  • Mizukami N, Clark MP, Gutmann ED, Mendoza PA, Newman AJ, Nijssen B et al (2016) Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: statistically downscaled forcing data and hydrologic models. J Hydrometeorol 17(1):73–98

    Article  Google Scholar 

  • Payne JT, Wood AW, Hamlet AF, Palmer RN, Lettenmaier DP (2004) Mitigating the effects of climate change on the water resources of the Columbia River basin. Clim Change 62(1):233–256

    Article  Google Scholar 

  • Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376

    Article  Google Scholar 

  • Pierce DW, Cayan DR, Das T, Maurer EP, Miller NL, Bao Y et al (2013) The key role of heavy precipitation events in climate model disagreements of future annual precipitation changes in California. J Clim 26(16):5879–5896

    Article  Google Scholar 

  • Salathé EP (2003) Comparison of various precipitation downscaling methods for the simulation of streamflow in a rainshadow river basin. Int J Climatol 23(8):887–901

    Article  Google Scholar 

  • Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res Atmos 118(4):1716–1733

    Article  Google Scholar 

  • Sobie SR, Murdock TQ (2017) High-resolution statistical downscaling in southwestern British Columbia. J Appl Meteorol Climatol. https://doi.org/10.1175/JAMC-D-16-0287.1

    Article  Google Scholar 

  • Stoner AM, Hayhoe K, Yang X, Wuebbles DJ (2013) An asynchronous regional regression model for statistical downscaling of daily climate variables. Int J Climatol 33(11):2473–2494

    Article  Google Scholar 

  • Sunyer Pinya MA, Hundecha Y, Lawrence D, Madsen H, Willems P, Martinkova M et al (2015) Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe. Hydrol Earth Syst Sci 19(4):1827–1847

    Article  Google Scholar 

  • Tang JP, Niu XR, Wang SY, Gao HX, Wang XY, Wu J (2016) Statistical downscaling and dynamical downscaling of regional climate in China: present climate evaluations and future climate projections. J Geophys Res Atmos 121(5):2110–2129

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192

    Article  Google Scholar 

  • Trigo RM, Palutikof JP (1999) Simulation of daily temperatures for climate change scenarios over Portugal: a neural network model approach. Clim Res 13(1):45–59

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330(3):621–640

    Article  Google Scholar 

  • Vigaud N, Vrac M, Caballero Y (2013) Probabilistic downscaling of GCM scenarios over southern India. Int J Climatol 33(5):1248–1263

    Article  Google Scholar 

  • Vrac M, Stein ML, Hayhoe K, Liang XZ (2007) A general method for validating statistical downscaling methods under future climate change. Geophys Res Lett 34(18):L18701

    Article  Google Scholar 

  • Vrac M, Drobinski P, Merlo A, Herrmann M, Lavaysse C, Li L, Somot S (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat Hazards Earth Syst Sci 12(9):2769–2784

    Article  Google Scholar 

  • Wang L, Chen W (2013) Application of bias correction and spatial disaggregation in removing model biases and downscaling over China (in Chinese). Adv Earth Sci 28(10):1144–1153

    Google Scholar 

  • Wang L, Chen W (2014) A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int J Climatol 34(6):2059–2078

    Article  Google Scholar 

  • Wen X, Fang GH, Zhou L, Qi HS (2015) Regional climate change and its possible effects on river runoff in Qiantang River Basin—past and future. Fresenius Environ Bull 24(11B):3880–3894

    Google Scholar 

  • Wen X, Fang GH, Qi HS, Zhou L, Gao YQ (2016) Changes of temperature and precipitation extremes in China: past and future. Theor Appl Climatol 126(1–2):369–383

    Article  Google Scholar 

  • Werner AT (2011) BCSD downscaled transient climate projections for eight select GCMs over British Columbia. Pacific Climate Impacts Consortium, University of Victoria, Victoria, p 63

    Google Scholar 

  • Werner AT, Cannon AJ (2016) Hydrologic extremes—an intercomparison of multiple gridded statistical downscaling methods. Hydrol Earth Syst Sci 20(4):1483

    Article  Google Scholar 

  • Wetterhall F, Bárdossy A, Chen D, Halldin S, Xu CY (2006) Daily precipitation-downscaling techniques in three Chinese regions. Water Resour Res 42(11):116

    Article  Google Scholar 

  • Widmann M, Bretherton CS, Salathé EP Jr (2003) Statistical precipitation downscaling over the northwestern United States using numerically simulated precipitation as a predictor. J Clim 16(5):799–816

    Article  Google Scholar 

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21(4):530–548

    Article  Google Scholar 

  • Wilby RL, Wigley TML (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20(6):641–661

    Article  Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA. Available from: IPCC-DDC: http://www.ipcc-data.org/

  • Wilk MB, Gnanadesikan R (1968) Probability plotting methods for the analysis of data. Biometrika 55(1):1–17

    Google Scholar 

  • Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res Atmos 107:D20

    Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62(1):189–216

    Article  Google Scholar 

  • Wu J, Gao XJ (2013) A gridded daily observation dataset over China region and comparison with the other datasets (in Chinese). Chin J Geophys 56:1102–1111

    Google Scholar 

  • Zheng J, Ding L, Hao Z, Ge Q (2012) Extreme cold winter events in southern China during AD 1650–2000. Boreas 41(1):1–12

    Article  Google Scholar 

  • Zhou B, Wen QH, Xu Y, Song L, Zhang X (2014) Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J Clim 27(17):6591–6611

    Article  Google Scholar 

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Acknowledgements

The work is jointly funded by the National Key Research and Development Program of China (2018YFA0606003 and 2016YFC0202000: Task 2) and the National Natural Science Foundation of China (41875124, 91425304, 41575099 and 41275004). This work is also supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change. The authors would like to thank National Climate Center of China Meteorological Administration for the provision of high-resolution gridded observations (CN05.1), the World Climate Research Programme (https://esgf-node.llnl.gov/search/cmip5/) for providing the CMIP5 data, and Michelangeli et al. (2009) for developing and making available the R-package “CDFt”. The statistical downscaling in this paper has been performed on the computing facilities in the High Performance Computing Center (HPCC) of Nanjing University.

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  1. CMA-NJU Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, 163 Xianlin Road, Nanjing, China

    Yi Yang, Jianping Tang & Shuyu Wang

  2. Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Zhe Xiong

  3. Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China

    Jian Yuan

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  1. Yi Yang
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Correspondence to Jianping Tang.

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Yang, Y., Tang, J., Xiong, Z. et al. An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations. Clim Dyn 53, 4629–4649 (2019). https://doi.org/10.1007/s00382-019-04809-x

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