Climate Dynamics

, Volume 46, Issue 5–6, pp 1991–2023 | Cite as

Credibility of statistical downscaling under nonstationary climate

  • Kaustubh Salvi
  • Subimal Ghosh
  • Auroop R. Ganguly


Statistical downscaling (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.


Statistical downscaling Stationarity Climate change 



The authors thank the anonymous reviewers for valuable comments. KS and SG acknowledge funding from the Space Technology Cell of Indian Institute of Technology Bombay and Indian Space Research Organization while ARG acknowledges support from the United States National Science Foundation (NSF) Expeditions in Computing Grant Award No. 1029166 titled “Understanding climate change: a data driven approach”. The collaborative activity between IIT Bombay and Northeastern University was supported through Interdisciplinary Program in Climate Studies, IIT Bombay, funded by Department of Science and Technology, Government of India. The authors thank Amey Pathak of the Indian Institute of Technology, Bombay, as well as Udit Bhatia, Thomas Vandal and Evan Kodra, all of Northeastern University, for helpful suggestions.

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  1. Anandhi A, Srinivas VV, Ravi S, Nanjundiah Nageshkumar D (2008) Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 28(3):401–420CrossRefGoogle Scholar
  2. Barker HW, Cole JNS, Morcrette JJ, Pincus R, Raisanen P, Salzen KV, Vaillancourt PA (2008) The Monte Carlo independent column approximation: an assessment using several global atmospheric models. Q J R Meteorol Soc 134:1463–1478CrossRefGoogle Scholar
  3. Bernett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438:303–309. doi: 10.1038/nature04141 CrossRefGoogle Scholar
  4. Bogardi I, Matyasovszky I, Bardossy A, Duckstein L (1993) Application of a space-time stochastic model for daily precipitation using atmospheric circulation patterns. J Geophys Res 98(D9):1653–1667Google Scholar
  5. Bromwich DH, Fogt RL (2004) Strong trends in the skill of the ERA-40 and NCEP–NCAR reanalyses in the high and midlatitudes of the Southern Hemisphere, 1958–2001. J Clim 17:4603–4619. doi: 10.1175/3241.1 CrossRefGoogle Scholar
  6. Busuioc A, von Storch H, Schnur R (1999) Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions. J Clim 12:258–272CrossRefGoogle Scholar
  7. Chaboureau JP, Bechtold P (2005) Statistical representation of clouds in a regional model and the impact on the diurnal cycle of convection during Tropical Convection, Cirrus and Nitrogen Oxides (TROCCINOX). J Geophys Res 110:D17103. doi: 10.1029/2004JD005645 CrossRefGoogle Scholar
  8. Charles SP, Bates BC, Hughes JP (1999) A spatio-temporal model for downscaling precipitation occurrence and amounts. J Geophys Res 104(D24):657–669Google Scholar
  9. Chen H, Ding YH, He JH (2007) Reappraisal of Asian summer monsoon indices and the long-term variation of monsoon. Acta Meteorol Sin 21(2):168–178Google Scholar
  10. Chou C, Tu JY, Yu JY (2002) Interannual Variability of the western north Pacific summer monsoon: differences between ENSO and non-ENSO years. J Clim 16:2275–2287. doi: 10.1175/2761.1 CrossRefGoogle Scholar
  11. Crane RG, Hewitson BC (1998) Doubled CO2 climate change scenarios for the Susquehanna basin: precipitation. Int J Climatol 18:65–76CrossRefGoogle Scholar
  12. DelSole T, Chang P (2003) Predictable component analysis, canonical correlation analysis, and autoregressive models. J Atmos Sci 60:409–416. doi: 10.1175/1520-0469(2003)060<0409:PCACCA>2.0.CO;2 CrossRefGoogle Scholar
  13. Dobler A, Ahrens B (2011) Four climate change scenarios for the Indian summer monsoon by the regional climate model COSMO-CLM. J Geophys Res 116(D24):1–13. doi: 10.1029/2011JD016329 Google Scholar
  14. Duan J, McIntyre N, Onof C (2013) Resolving non-stationarity in statistical downscaling of precipitation under climate change scenarios. In: BHS Eleventh National Symposium, Hydrology for a changing world, Dundee 2012. ISBN: 1903741181, doi:  10.7558/bhs.2012.ns16
  15. Gautam R, Hsu NC, Lau KM, Tsay SC, Kafatos M (2009) Enhanced pre-monsoon warming over the Himalayan–Gangetic region from 1979 to 2007. Geophys Res Lett 36:L07704. doi: 10.1029/2009GL037641 Google Scholar
  16. Ghosh S (2010) SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output. J Geophys Res 115:D22102. doi: 10.1029/2009JD013548 CrossRefGoogle Scholar
  17. Ghosh S, Mujumdar PP (2006) Future rainfall scenario over Orissa with GCM projections by statistical downscaling. Current 90(3):396–404Google Scholar
  18. Gillies RR, Wang SY, Huang WR (2012) Observational and supportive modeling analyses of winter precipitation change in China over the last half century. Int J Climatol 32:747–758. doi: 10.1002/joc/2303 CrossRefGoogle Scholar
  19. Glotter M, Elliott J, McInerney D, Best N, Foster I, Moyer EJ (2014) Evaluating the utility of dynamical downscaling in agricultural impacts projections. Proc Natl Acad Sci USA 111(24):8776–8781. doi: 10.1073/pnas.1314787111 CrossRefGoogle Scholar
  20. Goswami BN (2000) Comments on “Choice of south Asian summer monsoon indices”. Bull Am Meteorol Soc 81(4):821–822CrossRefGoogle Scholar
  21. Goswami BN, Krishnamurthy V, Annamalai H (1999) Abroadscale circulation index for the interannual variability of the Indian summer monsoon. Q J R Meteorol Soc 125(554):611–633CrossRefGoogle Scholar
  22. Groppelli B, Bocchiola D, Rosso R (2011) Spatial downscaling of precipitation from GCMs for climate change projections using random cascades: a case study in Italy. Water Resour Res 47(3):1–18. doi: 10.1029/2010WR009437 Google Scholar
  23. Han Z, Zhou T (2012) Assessing the quality of APHRODITE high-resolution daily precipitation dataset over contiguous China. Chin J Atmos Sci 36(2):361–373. doi: 10.3878/j.issn.1006-9895.2011.11043 (in Chinese) Google Scholar
  24. Hay LE, McCabe GJ, Wolock DM, Ayers MA (1991) Simulation of precipitation by weather type analysis. Water Resour Res 27(4):493–501CrossRefGoogle Scholar
  25. Hertig E, Jacobeit J (2008) Assessments of Mediterranean precipitation changes for the 21st century using statistical downscaling techniques. Int J Climatol 28:1025–1045CrossRefGoogle Scholar
  26. 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. doi: 10.1002/jgrd.50112 CrossRefGoogle Scholar
  27. Hewitson BC, Crane RG (1992) Large-scale atmospheric controls on local precipitation in tropical Mexico. Geophys Res Lett 19(18):1835–1838. doi: 10.1029/92GL01423 CrossRefGoogle Scholar
  28. Hines KM, Bromwich DH, Marshall GJ (2000) Artificial surface pressure trends in the NCEP-NCAR reanalysis over the Southern Ocean and Antarctica. J Clim 13:3940–3952CrossRefGoogle Scholar
  29. Hughes JP, Guttorp P (1994a) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30(5):1535–1546CrossRefGoogle Scholar
  30. Hughes JP, Guttorp P (1994b) Incorporating spatial dependence and atmospheric data in a model of precipitation. J Appl Meteorol 33:1503–1515CrossRefGoogle Scholar
  31. Hughes JP, Lettenmair DP, Guttorp P (1993) A stochastic approach for assessing the effect of changes in synoptic circulation patterns on gauge precipitation. Water Resour Res 29(10):3303–3315CrossRefGoogle Scholar
  32. Huth R (1997) Potential of continental-scale circulation for the determination of local daily surface variables. Theor Appl Climatol 56:165–186CrossRefGoogle Scholar
  33. IPCC (2007) Climate change 2007: the physical science base. In: Solomon S, Qin D, Manning M (eds.) Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate ChangeGoogle Scholar
  34. IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Contribution of Working Group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  35. Jenkinsa CN, Van Houtan KS, Pimm SL, Sexton JO (2015) US protected lands mismatch biodiversity priorities.
  36. Jimenez-Valverde A, Lobo JM, Hortal J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib. doi: 10.1111/j.1472-4642.2008.00496.x Google Scholar
  37. Jones C, Giorgi F, Asrar G (2011) the coordinated regional downscaling experiment: CORDEX—an international downscaling link to CMIP5, CLIVAR Exch 56(16–2): 34–40.
  38. Junk J, Matzarakis A, Ferrone A, Krein A (2014) Evidence of past and future changes in health-related meteorological variables across Luxembourg. Air Qual Atmos Health 7:71–81. doi: 10.1007/s11869-013-0229-4 CrossRefGoogle Scholar
  39. Kalnay E, Kanamitsu M, Kistler R et al (1996) The NCEP/NCAR 40-years reanalysis project. Bull Am Meteorol Soc 77(3):437–471CrossRefGoogle Scholar
  40. Kanamitsu M, Ebisuzaki W, Woollen J, Yang S, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP–DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643. doi: 10.1175/BAMS-83-11-1631 CrossRefGoogle Scholar
  41. Kannan S, Ghosh S (2013) A nonparametric Kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin. Water Resour Res. doi: 10.1002/wrcr.20118 Google Scholar
  42. Kannan S, Ghosh S, Mishra V, Salvi K (2014) Uncertainty resulting from multiple data usage in statistical downscaling. Geophys Res Lett 41:4013–4019. doi: 10.1002/2014GL060089 CrossRefGoogle Scholar
  43. Kerr RA (2013) Forecasting regional climate change flunks its first test. Science 339:638. doi: 10.1126/science.339.6120.638 CrossRefGoogle Scholar
  44. Khalili MF, Brissette Leconte R (2009) Stochastic multi-site generation of daily weather data. Stoch Environ Res Risk Assess 23:837–849. doi: 10.1007/s00477-008-0275-x CrossRefGoogle Scholar
  45. Kishtawal CM, Niyogi D, Tewari M, PielkeSr RA, Shepherd JM (2010) Urbanization signature in the observed heavy rainfall climatology over India. Int J Climatol 30:1908–1916CrossRefGoogle Scholar
  46. Knutti R, Sedláček J (2012) Robustness and uncertainties in the new CMIP5 climate. Nat Clim Change 2:587–595. doi: 10.1038/nclimate1495 CrossRefGoogle Scholar
  47. KrishnaKumar K, Rajagopalan B, Cane MA (1999) On the weakening relationship between the Indian monsoon and ENSO. Science 284(5423):2156–2159. doi: 10.1126/science.284.5423.2156 CrossRefGoogle Scholar
  48. Kumar D, Kodra E, Ganguly AR (2014) Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation. Clim Dyn 43(9–10):2491–2518CrossRefGoogle Scholar
  49. Langenbrunner B, Neelin JD (2013) Analyzing ENSO Teleconnections in CMIP Models as a measure of model fidelity in simulating precipitation. J Clim 26:4431–4446. doi: 10.1175/JCLI-D-12-00542.1 CrossRefGoogle Scholar
  50. Laprise R (2008) Regional climate modelling. J Comput Phys 227:3641–3666. doi: 10.1016/ CrossRefGoogle Scholar
  51. Laprise R (2014) Comment on “The added value to global model projections of climate change by dynamical downscaling: a case study over the continental U.S. using the GISS-ModelE2 and WRF models” by Racherla et al. J Geophys Res Atmos 119(3877–3881):2013J. doi: 10.1002/D019945 Google Scholar
  52. Li J (2002) Accounting for unresolved clouds in a 1D infrared radiative transfer code. Part I: solution for radiative transfer, cloud scattering and overlap. J Atmos Sci 59:3302–3320CrossRefGoogle Scholar
  53. Li J, Barker HW (2002) Accounting for unresolved clouds in a 1D infrared radiative transfer code. Part II: cloud subgrid-scale variability. J Atmos Sci 59:3321–3339CrossRefGoogle Scholar
  54. Li J, Barker HW (2005) A radiation algorithm with correlated k-distribution. Part I: local thermal equilibrium. J Atmos Sci 62:286–309CrossRefGoogle Scholar
  55. Li J, Ding R (2011) Temporal-spatial distribution of atmospheric predictability limit by local dynamical analogs. Mon Weather Rev 139:3265–3283. doi: 10.1175/MWR-D-10-05020.1 CrossRefGoogle Scholar
  56. Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res. doi: 10.1029/2009JD012882 Google Scholar
  57. Lin W, Frere CH, Karczmarski L, Xia J, Gui D, Wu Y (2014) Phylogeography of the finless porpoise (genus Neophocaena): testing the stepwise divergence hypothesis in the northwestern Pacific. Sci Rep. doi: 10.1038/srep06572 Google Scholar
  58. Litta AJ, Idicula SM, Mohanty UC (2011) A comparative study of convective parameterization schemes in WRF-NMM model. Int J Comput Appl 33(6):32–40Google Scholar
  59. Masello JF, Montano V, Quillfeldt P, Nuhlickova S, Wikelski M, Moodley Y (2015) The interplay of spatial and climaticlandscapes in the genetic distribution ofa South American parrot. J Biogeogr. doi: 10.1111/jbi.12487 Google Scholar
  60. Maurer EP, O’Donnell GM, Lettenmaier DP, Roads JO (2001) Evaluation of the land surface water budget in NCEP/NCAR and NCEP/DOE reanalyses using an off-line hydrologic model. J Geophys Res 106(D16):17841–17862CrossRefGoogle Scholar
  61. Mehrotra R, Sharma A (2005) A nonparametric nonhomogeneous hidden Markov model for downscaling of multisite daily rainfall occurrences. J Geophys Res 110:D16108. doi: 10.1029/2004JD005677 CrossRefGoogle Scholar
  62. Mehrotra R, Sharma A (2006) A nonparametric stochastic downscaling framework for daily rainfall at multiple locations. J Geophys Res 111:D15101. doi: 10.1029/2005JD006637 CrossRefGoogle Scholar
  63. Mishra V, Lettenmaier DP (2011) Climatic trends in major U.S. urban areas, 1950–2009. Geophys Res Lett 38:L16401. doi: 10.1029/2011GL048255 CrossRefGoogle Scholar
  64. Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284CrossRefGoogle Scholar
  65. Nadaraya EA (1964) On estimating regression. Theory Probab Appl 10:186–190CrossRefGoogle Scholar
  66. Onogi K, Tsutsui J, Koide H, Sakamoto M, Kobayashi S, Hatsushika H, Matsumoto T, Yamazaki N, Kamahori H, Takahashi K, Kadokura S, Wada K, Kato K, Oyama R, Ose T, Mannoji N, Taira R (2007) The JRA-25 reanalysis. J Meteorol Soc Jpn 85:369–432CrossRefGoogle Scholar
  67. Özelkan EC, Duckstein L (1996) Relationship between monthly atmospheric circulation patterns and precipitation: fuzzy logic and regression approaches. Water Resour Res 32(7):2097–2103CrossRefGoogle Scholar
  68. Parthasarathy B, Kumar KR, Kothawale DR (1992) Indian summer monsoon rainfall indexes—1871–1990. Meteorol Mag 121(1441):174–186Google Scholar
  69. Parthasarathy B, Rupakumar K, Munot AA (1996) Homogeneous regional summer monsoon rainfall over India: interannual variability and teleconnections, Research report no. RR-070Google Scholar
  70. Pielke RA, Adegoke J, Beltran-Przekurat A, Hiemstra A, Lin J, Nair US, Niyogi D, Nobis TE (2007) Tellus 59B:587–601. doi: 10.1111/j.1600-0889.2007.00251.x CrossRefGoogle Scholar
  71. Pincus R, Barker HW, Morcrette JJ (2003) A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J Geophys Res 108:4376Google Scholar
  72. Pinto JO, Monaghan AJ, Monache LD, Vanvyve E, Rife DL (2014) Regional assessment of sampling techniques for more efficient dynamical climate downscaling. J Clim 27:1524–1538. doi: 10.1175/JCLI-D-13-00291.1 CrossRefGoogle Scholar
  73. Racherla PN, Shindell DT, Faluvegi GS (2012) The added value to global model projections of climate change by dynamical downscaling: a case study over the continental U.S. using the GISS-ModelE2 and WRF models. J Geophys. doi: 10.1029/2012JD018091 Google Scholar
  74. Raje D, Mujumdar PP (2009) A conditional random field–based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin. Water Resour Res 45:W10404. doi: 10.1029/2008WR007487 Google Scholar
  75. Ramesh KV, Goswami P (2014) Assessing reliability of regional climate projections: the case of Indian monsoon. Sci Rep. doi: 10.1038/srep04071 Google Scholar
  76. Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190CrossRefGoogle Scholar
  77. Road J, Betts A (1999) NCEP–NCAR and ECMWF reanalysis surface water and energy budgets for the Mississippi river basin. J Hydrometeorol 1:88–94CrossRefGoogle Scholar
  78. Ryu J, Hayhoe K (2013) Understanding the sources of Caribbean precipitation biases in CMIP3 and CMIP5 simulations. Clim Dyn. doi: 10.1007/s00382-013-1801-1 Google Scholar
  79. Salvi K, Kannan S, Ghosh S (2013) High resolution multi-site daily projections in India with statistical downscaling for climate change impacts assessment. J Geophys Res Atmos. doi: 10.1002/jgrd50280 Google Scholar
  80. Schmith T (2008) Stationarity of regression relationships: application to empirical downscaling. Notes Corresp. doi: 10.1175/2008JCLI1910.1 Google Scholar
  81. Shashikanth K, Salvi K, Ghosh S, Rajendran K (2013) Do CMIP5 simulations of Indian summer Monsoon rainfall differ from those of CMIP3? Atmos Sci Lett. doi: 10.1002/asl2.466 Google Scholar
  82. Shastri H, Paul S, Ghosh S, Karmakar S (2015) Impacts of urbanization on Indian summer Monsoon rainfall. Extremes. doi: 10.1002/2014JD022061 Google Scholar
  83. Slonosky VC, Jones PD, Davies TD (2001) Atmospheric circulation and surface temperature in Europe from the 18th century to 1995. Int J Climatol 21:63–75CrossRefGoogle Scholar
  84. Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2012) The Asian summer monsoon: an intercomparison of CMIP5 vs CMIP3 simulations of the late 20th century. Clim Dyn. doi: 10.1007/s00382-012-1607-6 Google Scholar
  85. Stickler A, Brönnimann S (2011) Significant bias of the NCEP/NCAR and twentieth century reanalyses relative to pilot balloon observations over the West African monsoon region (1940–1957). Q J R Meteorol Soc. doi: 10.1002/qj.854 (published online) Google Scholar
  86. Stocker TF, Qin D, Plattner GK, Alexander LV et al. (2013) Technical summary. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Doschung J, Nauels A, Xia Y, Bex V, Midgley PM (Eds) Climate Change 2013: the physical science basis. Contribution of Working Group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 33–115, doi: 10.1017/CBO9781107415324.005
  87. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  88. Timmermann A, Oberhuber J, Bacher A, Esch M, Latif M, Roeckner E (1999) Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature 398:694–697CrossRefGoogle Scholar
  89. Tojo B, Kotera A, Nakai K, Nagano T, Kobayashi S, Moji K (2011) Evaluation of recent forest cover change in Savannakhet Province, Lao PDR, using AVNIR-2 and MODIS satellite images. Global Environ Res V15N2:119–130Google Scholar
  90. Underwood FM (2009) Describing long-term trends in precipitation using generalized additive models. J Hydrol 364(3–4):285–297CrossRefGoogle Scholar
  91. Uppala SM, Kållberg PW, Simmons AJ et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012. doi: 10.1256/qj.04.176 CrossRefGoogle Scholar
  92. Wang B (2000) Comments on “Choice of South Asian summer monsoon indices”—reply. Bull Am Meteorol Soc 81(4):822–824CrossRefGoogle Scholar
  93. Wang B, Fan Z (1999) Choice of South Asian summer monsoon indices. Bull Am Meteorol Soc 80(4):629–638CrossRefGoogle Scholar
  94. Wang SY, Gillies RR (2013) Influence of the Pacific quasi-decadal oscillation on the monsoon precipitation in Nepal. Clim Dyn 40:95–107. doi: 10.1007/s00382-012-1376-2 CrossRefGoogle Scholar
  95. Wang QJ, Robertson DE, Chiew FHS (2009) A Bayesian joint probability modelling approach for seasonal forecasting of streamflows at multiple sites. Water Resour Res 45(5):1–18. doi: 10.1029/2008WR007355 Google Scholar
  96. Watson GS (1964) Smooth regression analysis. Sankhya Ser A 26:359–372Google Scholar
  97. Webster PJ, Yang S (1992) Monsoon and ENSO—selectively interactive systems. Q J R Meteorol Soc 118(507):877–926CrossRefGoogle Scholar
  98. Wilby RL (1994) Stochastic weather type simulation for regional climate change impact assessment. Water Resour Res 30:3395–3403CrossRefGoogle Scholar
  99. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548CrossRefGoogle Scholar
  100. Wilby RL, Hassan H, Hanaki K (1998) Statistical downscaling of hydro-meteorological variables using general circulation model output. J Hydrol 205:1–19CrossRefGoogle Scholar
  101. Wilby RL, Hey LE, Leavesly GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado. J Hydrol 225:67–91CrossRefGoogle Scholar
  102. Wilby RL, Tomlinson OJ, Dawson CW (2003) Multi-site simulation of precipitation by conditional resampling. Clim Res 23:183–194CrossRefGoogle Scholar
  103. Wilby RL, Charles SP, Zorita E et al (2004) The guidelines for use of climate scenarios developed from statistical downscaling methods. In: Supporting material of the Intergovernmental Panel on Climate Change (IPCC), Prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA). (
  104. Wilks DS (1999) Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain. Agric For Meteorol 96:85–101CrossRefGoogle Scholar
  105. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, NCEAS Predicting Species Distributions Working Group (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763–773CrossRefGoogle Scholar
  106. Xue Y, Janjic Z, Dudhia J, Vasic R, Sale FD (2014) A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability. Atmos Res 147–148(2014):68–85. doi: 10.1016/j.atmosres.2014.05.001 CrossRefGoogle Scholar
  107. Yatagai A, Arakawa O, Kamiguchi K, Kawamoto H, Nodzu MI, Hamada A (2009) A 44 year daily gridded precipitation data set for Asia based on a dense network of rain gauges. SOLA 5:137–140. doi: 10.2151/sola.2009-035 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kaustubh Salvi
    • 1
  • Subimal Ghosh
    • 1
    • 2
  • Auroop R. Ganguly
    • 2
    • 3
  1. 1.Department of Civil EngineeringIndian Institute of Technology BombayPowai, MumbaiIndia
  2. 2.Interdisciplinary Program in Climate StudiesIndian Institute of Technology BombayPowai, MumbaiIndia
  3. 3.Sustainability and Data Sciences Laboratory, Civil and Environmental EngineeringNortheastern UniversityBostonUSA

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