Water resource managers are interested in planning for future climate change scenarios, but global climate models are too coarse for water resource planning and running scenarios through dynamic downscaled regional climate models can be overly time-consuming. For this experiment, we conceptually illustrate that regional climate models can reproduce observed data for the San Francisco area, skipping a time-intensive intermediate step. To determine whether skipping the step would negatively affect output, we downscaled 13 months of National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NNRP2) data from native to 50, 40, and 20-km resolution using the regional climate model RegCM3. Outputs relevant to water planners, temperature and precipitation were compared with a high resolution observed dataset, which indicated that this configuration of RegCM3 can produce downscaled data with high correlations to observed data for this domain. The high correlations indicate that this domain can be simulated with a high spatial resolution ratio (1:14), without the need for the intermediate step. This study is a proof of concept that high resolution data can be obtained more efficiently for water agencies considering possible climate scenarios in planning for their future water supply. However, additional analysis is necessary before information can be obtained from downscaled models for decision-relevant use.

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