A multi-resolution analysis of historical and future precipitation variability across the western United States&#160;
Authors: Bjarke Nels , Livneh Ben , Barsugli Joseph , Quan Xiao Wei , Hoerling Martin 
Published: March 2021

&lt;p&gt;Evaluating the future of surface water availability in the western United States requires a robust analysis of the projected trends in precipitation variability within the new generation of global climate model (GCM) simulations. To understand the reliability of future projections, we first construct a historical baseline (1950-2014) of the precipitation climatology and &amp;#160;contribution of heavy precipitation events to the total annual precipitation from an ensemble of in-situ (Global Historical Climatology Network (GHCN)) and gridded precipitation products (Abatzoglou, 2013; Livneh et al., 2015; Newman et al., 2015). This historical baseline is used to evaluate the representation of precipitation variability during the historical period of GCM simulations from the CMIP6 HighResMIP and ScenarioMIP ensembles as well as the multi-resolution, factual-counterfactual ensemble of CAM5 simulations. We frame our analysis in the context of water resources by using a collection of large basins across the western US to demonstrate that the role of GCM resolution in the representation of precipitation variability is highly dependent on regional differences in topographical controls and dominant climatological drivers of precipitation. In most regions, we find that the highest-resolution GCM simulations (25-50 km) portray realistic occurrences of heavy precipitation events when compared to gridded historical precipitation at the same spatial resolution, whereas coarser GCM simulations (100-200 km) tend to distribute precipitation more evenly throughout the year than expected. When compared to the historical period (1950-2014), future projections (2014-2050) from both HighResMIP and ScenarioMIP ensembles produce more variable precipitation with a higher fraction of the annual precipitation falling in heavy precipitation events. &amp;#160;Furthermore, we explore methods for constraining uncertainty in the projection of future precipitation variability across the Western US using a statistical assessment of the historical GCM simulations compared to the historical baseline.&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121&amp;#8211;131. https://doi.org/10.1002/joc.3413&lt;/p&gt;&lt;p&gt;Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., &amp; Brekke, L. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950&amp;#8211;2013. Scientific Data, 2(1), 1&amp;#8211;12. https://doi.org/10.1038/sdata.2015.42&lt;/p&gt;&lt;p&gt;Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., &amp; Duan, Q. (2015). Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences, 19(1), 209&amp;#8211;223. https://doi.org/10.5194/hess-19-209-2015&lt;/p&gt;