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. Understanding the unfolding challenges of climate change relies on climatemodels, many of which have large summer warm and dry biases over NorthernHemisphere continental midlatitudes. This work, with the example of themodel used in the updated version of the weather@home distributed climatemodel framework, shows the potential for improving climate model simulationsthrough a multiphased parameter refinement approach, particularly overthe northwestern United States (NWUS). Each phase consists of (1) creating aperturbed parameter ensemble with the coupled global–regional atmosphericmodel, (2) building statistical emulators that estimate climate metrics asfunctions of parameter values, (3) and using the emulators to further refinethe parameter space. The refinement process includes sensitivity analyses toidentify the most influential parameters for various model output metrics;results are then used to cull parameters with little influence. Three phasesof this iterative process are carried out before the results are consideredto be satisfactory; that is, a handful of parameter sets are identified thatmeet acceptable bias reduction criteria. Results not only indicate that74 % of the NWUS regional warm biases can be reduced by refining globalatmospheric parameters that control convection and hydrometeor transport,as well as land surface parameters that affect plant photosynthesis, transpiration,and evaporation, but also suggest that this iterative approach to perturbedparameters has an important role to play in the evolution of physicalparameterizations.

Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation