Change Factor Based Downscaling of Precipitation Through Neyman-Scott Rectangular Pulse based Rainfall Field Generators

We present a multi-site weather generator with a stochastic rainfall field generator (RFG) at its core. The weather generator is developed with the motive to produce downscaled projections for the future by utilizing the UKCP18 projections and a suite of climate models from the CMIP5/6 archive. The rainfall fields are sampled from the spatio-temporal Neyman-Scott Rectangular Pulse (NSRP) process. When considering a single site, the NSRP model parameterizes storm arrivals as a poisson process and storm separation time as exponential distribution. Each storm is assigned a certain number of raincells (a poisson random number) with each raincell having a duration and intensity which are exponentially distributed. For a multi-site model, additional considerations are made which include the radius of raincell parameterised by exponential distribution and the raincell density as a uniform poisson process (which is a replacement to the raincell generation process of single site model). The RFG has shown its efficacy in capturing the statistics of the observed rainfall across point and catchment scales which include mean monthly rainfall totals, daily variance, skewness, lag-1 autocorrelation, dry-day proportion and daily annual maximum in addition to capturing intergauge correlations. . Following the calibration and testing of the NSRP-based RFG, the other weather variables such as temperature and wind speed are ascertained through regression relationships by considering wet and dry transition states of rainfall. With the RFG established, climate model downscaling is performed by computing multiplicative and additive change factors for rainfall and temperature respectively. The RFG paramaters are perturbed by the computed change factor(s) to derive downscaled projections of precipitation thereby offering multiple plausible future scenarios in addition to a band of uncertainty associated with the projections. These projections can be further translated to hydrological responses by leveraging hydrological models thereby aiding in climate change impact assessment and adaptation.