Statistical Downscaling of Rainfall using Large-Scale Predictors: Dynamic Model Outputs vs. Reanalysis Data


  • Yan Lu School of Civil & Environmental Engineering, Nanyang Technological University
  • P.V. Mandapaka School of Civil & Environmental Engineering, Nanyang Technological University, Singapore 639798
  • C.C. Kuo Department of Civil and Environmental Engineering, School of Mining & Petroleum Engineering, Faculty of Engineering, University of Alberta, Canada
  • T. Y. Gan Department of Civil and Environmental Engineering, School of Mining & Petroleum Engineering, Faculty of Engineering, University of Alberta, Canada
  • Xiaosheng Qin Nanyang Technological University


A warmer climate is expected to lead to more serious natural disasters, such as heavy storms, prolonged droughts and frequent floods. For a high-density urban region, the flash flood problem may become worse due to possible increasing frequency and magnitude of short-duration rainfalls in the future. A Global Circulation Model (GCM) is a powerful tool to assess the climate change impact. However, the resolution of a GCM output is generally too coarse to be applicable to small regions directly. Two types of approaches, dynamical and statistical downscaling, could be used for bridging the gap between GCM and local climate information. Compared with dynamical downscaling, the statistical approach is more flexible and computationally less intensive. In addition, statistical downscaling tools may be sensitive to the resolution of large-scale predictors. In this study, two downscaling approaches are compared. The first is to use a statistical method (Automatic Statistical Downscaling, ASD) directly to downscale large-scale predictors (i.e. ERA-Interim Reanalysis data) to local rainfall. The second is to combine a dynamical (i.e. MM5) and a statistical method (ASD) to generate the station-level data. The study site is the City of Edmonton and the resolutions of large-scale GCM predictors and dynamical model output are about 150 km and 27 km, respectively. The results show that the downscaled results based on predictors from MM5 is better than that from ERA-Interim, in terms of both accuracy and uncertainty range. 

Author Biographies

Yan Lu, School of Civil & Environmental Engineering, Nanyang Technological University

PhD candidate, School of Civil & Environmental Engineering, Nanyang Technological University

Xiaosheng Qin, Nanyang Technological University

Assistant Professor


Benestad, R.E., Nychka, D., Mearns, L.O. (2012). Spatially and temporally consistent prediction of heavy precipitation from mean values. Nature Climate Change 2, 544-547, doi: 10.1038/NCLIMATE1497.

Caldwell, P., Chin, H.N., Bader, D.C., Bala, G. (2009). Evaluation of a WRF dynamical downscaling simulation over California. Climatic Change 95, 499-521.

Chandler, R.E., Wheater, H.S. (2002). Analysis of rainfall variability using generalized linear models: A case study from the west of Ireland. Water Resources Research 38, 1192, doi: 10.1029/2001WR000906.

Chen, J., Brissette, F.P., Leconte, R. (2012). Coupling statistical and dynamical downscaling for spatial downscaling of precipitation. Climatic Change 114, 509-526.

Collins, W.D., Hack, J.J., Boville, B.A., Rasch, P.J. and others (2004). Description of the NCAR Community Atmosphere Model (CAM3.0). Technical Note TN-464+STR, National Center for Atmospheric Research, Boulder, CO.

Coumou, D. and Rahmstorf, S. (2012). A decade of weather extremes. Nature Climate Change 2, 491-496, doi: 10.1038/NCLIMATE1452.

Dai, A. (2013). Increaseing drought under global warming in observations and models. Nature Climate Change 3, 52-58, doi: 10.1038/NCLIMATE1633.

Fowler, H.J., Blenkinsop. S., Tebaldi, C. (2007). Review Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology 27, 1547-1578.

Hanrahan, J., Kuo, C. C., and Gan, T. Y. (2014), Configuration and validation of a mesoscale atmospheric model for simulating summer rainfall in Alberta, Int. J. of Climatology, RMS, DOI: 10.1002/joc.4011.

Hessami, M., Gachon, P., Ouarda, T.B.M.J., St-Hilaire, A. (2008). Automated regression-based statistical downscaling tool. Environmental Modelling & Software 23, 813-834.

Heikkilä, U., Sandvik, A., Sorteberg, A. (2010). Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model. Clim Dyn, doi: 10.1007/s00382-010-0928-6.

Hwang, S., Graham, W., Hernández, J.L., Martinez, C., Jones, J.W., Adams, A. (2011). Quantitative spatiotemporal evaluation of dynamically downscaled MM5 precipitation predictions over the Tampa Bay Region, Florida. J. Hydrometeor, 12, 1447–1464. doi:

Kiehl, J.T., Gent, P.R. (2004). The Community Climate System Model, version 2. Journal of Climate 17, 3666-3682.

Kharin, V.V., Zwier, F.W., Zhang, X. (2005). Intercomparison of near-surface temperature and precipitation extremes in AMIP-2 simulations, reanalysis, and observations. Journal of Climate 18, 5201-5223.

Kuo, C.C., Gan, T.Y., Hanrahan, J.L. (2014). Precipitation frequency analysis based on regional climate simulations in Central Alberta. Journal of Hydrology 510, 436-446.

Orskaug, E., Scheel, I., Frigessi, A., Guttorp, P., Haugen, J.E., Tveito, O.E., Hang, O. (2011). Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland. Tellus 63A, 746-756.

Pope, V.D., Gallani, M.L., Rowntree, P.R., Stratton, R.A. (2000). The impact of new physical parameterizations in the Hadley Centre climate model – HadAM3. Climate Dynamics 16: 123-146.

Wilby, R.L., Wigley, T.M.L. (1997). Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography 21, 530-548.

Wilby, R.L., Dawson, C.W., Barrow, E.M. (2002). SDSM-a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software 17, 147-159.