Probabilistic estimates of future changes in 1 California temperature and precipitation using 2 statistical and dynamical downscaling

25 Sixteen global general circulation models were used to develop probabilistic projections of 26 temperature (T) and precipitation (P) changes over California by the 2060s. The global models 27 were downscaled with two statistical techniques and three nested dynamical regional climate 28 models, although not all global models were downscaled with all techniques. Both monthly and 29 daily timescale changes in T and P are addressed, the latter being important for a range of 30 applications in energy use, water management, and agriculture. The T changes tend to agree more 31 across downscaling techniques than the P changes. Year-to-year natural internal climate variability 32 is roughly of similar magnitude to the projected T changes. In the monthly average, July 33 temperatures shift enough that that the hottest July found in any simulation over the historical 34 period becomes a modestly cool July in the future period. Januarys as cold as any found in the 35 historical period are still found in the 2060s, but the median and maximum monthly average 36 temperatures increase notably. Annual and seasonal P changes are small compared to interannual 37 or intermodel variability. However, the annual change is composed of seasonally varying changes 38 that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly 39 wetter conditions in the North of the state, while spring and autumn show less precipitation. The 40 dynamical downscaling techniques project increasing precipitation in the Southeastern part of the 41 state, which is influenced by the North American monsoon, a feature that is not captured by the 42 statistical downscaling.