Downscaling projections of Indian monsoon rainfall using a non-homogeneous hidden Markov model

Downscaled rainfall projections for the Indian summer monsoon are generated using a non-homogeneous hidden Markov model (NHMM) and information from both a dense observational dataset and an ensemble of general circulation models (GCMs). The projections are conditioned on two types of GCM information, corresponding approximately to dynamic and thermodynamic components of precipitation change. These have opposing effects, with a weakening circulation compensating not quite half of the regional precipitation increase that might otherwise be expected. GCM information is taken at the largest spatial scales consistent with regional physics and modelling constraints, while the NHMM produces a disaggregation consistent with the observed fine-scale spatiotemporal variability. Projections are generated using multimodel mean predictors, with intermodel dispersion providing a measure of the uncertainty due to GCM differences. The downscaled simulations exhibit small increases in the number of dry days, in the average length of dry spells, in mean daily intensity and in the exceedance frequency of nearly all daily rainfall percentiles