High-resolution CONUS-wide downscaled rainfall estimates (HRCDRE)

The spatiotemporal character of rainfall is particularly important for
hydrologic modeling, as well as hydroclimatic risk estimation and impact
assessment. Existing atmospheric reanalysis datasets offer extensive
record lengths and global coverage, but usually their spatial resolution
is coarse for distributed hydrologic simulations at small spatial scales.
On the other hand, the temporal coverage of high-resolution radar-based
rainfall estimates can be rather short for risk applications. To address
these shortcomings, we simultaneously bias-correct and downscale a
state-of-the-art atmospheric reanalysis (ERA5) rainfall dataset, using the
radar-based Stage IV precipitation product as fine resolution reference,
to develop an hourly CONUS-wide precipitation product over a 4-km grid,
which extends back to 1979. In this regard, we refine an existing
parametric quantile mapping framework based on a two-component theoretical
distribution model, where we impose continuity of the parametric forms via
optimal threshold selection to transition between higher and lower rain
rates. An evaluation over the probability frequency and time domains,
using NOAA’s raingauge measurements as benchmark, reveals that the
developed product benefits from the strengths of the calibration datasets,
demonstrating good performance and robust behavior over all studied time
periods and Köppen climate classification zones, including snow-prone
regions or areas where mesoscale convective systems become dominant. The
accuracy of the yielded high spatial-resolution rain rates, especially in
low probability events, shows that the developed product can be
effectively used for hydroclimatic risk applications and frequency
analysis, while its high temporal and spatial resolution makes it
particularly useful for distributed hydrologic modeling.