A dynamical alternative for simulating multi-scale high-resolution sub-daily space-time precipitation for future climates

How can climate model simulations for the future generate high-resolution sub-daily precipitation that could be trusted for a range of hydrologic design applications? This is a question that often provokes contradicting responses from climate scientists and hydrologists. Our study attempts to unify the knowledge gained from these two disciplines to present the first ever alternative for generating multi-scale (low-to-high frequency temporal persistence), high resolution (down to 1km if needed), sub-daily precipitation for future climates that is dynamically consistent with concurrent climate forcings (including atmospheric moisture, circulation patterns) and local topography. This dynamical precipitation generator contains three components. The first component is the raw temporal resolution climate field simulated using global climate models on the lower and the lateral boundaries of the spatial domain precipitation simulations are needed for. The second component is SDMBC, an innovative alternative for correcting systematic biases at Sub-Daily (SD) time steps, using the Multivariate Bias Correction (MBC) approach which corrects multivariate dependence, persistence and distributional attributes across variables that form the lateral and lower boundaries of the domain of interest. The third and final component is a Regional Climate Model (RCM), chosen to be the Weather Research and Forecasting (WRF) model for the present study, which uses the corrected lateral and lower boundary forcings generated from the second component of our framework, and generates dynamically consistent sub-daily precipitation along with other physically consistent atmospheric variables at high-resolution. It is shown here that use of this framework simulates precipitation fields that exhibit features consistent with observations including observed extremes, and storm events that are consistent with our expectations of how precipitation extremes will evolve in future (warmer) climates. A Python software (named SDMBC) that simplifies the implementation of the bias correction process is presented, and results are shown for simulations across the Australian domain. This software is now available from (https://pypi.org/project/sdmbc/) and can be used for applications over any domain worldwide in conjunction with WRF models that have been formulated independently.