Downscaling of precipitation for climate change scenarios: A support vector machine approach Shivam Tripathi a , V.V. Srinivas a, * , Ravi S. Nanjundiah b a Department of Civil Engineering, Indian Institute of Science, Bangalore 560 012, Karnataka, India b Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore 560 012, Karnataka, India Received 27 April 2005; received in revised form 22 March 2006; accepted 14 April 2006 Summary The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for sta- tistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, cli- mate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of pre- cipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.  c 2006 Elsevier B.V. All rights reserved. KEYWORDS Precipitation; Downscaling; Climate change; General circulation model (GCM); Support vector machine; Neural network; Hydroclimatology; India Introduction Recently, there is growth in scientific evidence that global climate has changed, is changing and will continue to 0022-1694/$ - see front matter  c 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2006.04.030 * Corresponding author. Tel.: +91 80 2293 2641; fax: +91 80 2360 0404. E-mail addresses: vvs@civil.iisc.ernet.in, vvsrinivas@yahoo.com (V.V. Srinivas). Journal of Hydrology (2006) 330, 621– 640 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jhydrol