Technical note: A Flexible Framework for Precision Truncation and Lossless Compression in WRF Simulations with Application over the United States

Abstract. As climate simulations generate increasingly large datasets, reducing storage demands without compromising scientific integrity has become a critical challenge. This study evaluates the effectiveness of precision truncation, applied prior to lossless compression, in balancing storage efficiency and fidelity within regional Weather Research and Forecasting (WRF) simulations over the United States. We examine input-only, output-only, and combined input–output truncation strategies across both routine meteorological variables and extreme precipitation indices. Results show that conventional atmospheric fields remain robust when outputs are truncated to 5 or 4 significant digits, keeping biases within acceptable limits. Wind speed is largely insensitive to truncation, temperature and humidity are more vulnerable under aggressive output truncation (3 significant digits). Precipitation shows mixed responses, with deviations dominated by input perturbations. Extreme precipitation indices display more complex sensitivities: percentile- and maximum-based indices are highly susceptible to nonlinear, regionally heterogeneous biases under input truncation, whereas frequency- and intensity-based indices respond more systematically to output truncation, with substantial distortions emerging at 3 digits. These findings demonstrate that truncation strategies cannot be applied uniformly but must be tailored to variable type and diagnostic. Within this study, output-only truncation emerges as the most reliable strategy, with 4 significant digits identified as a safe lower bound and 5 digits preferable when fidelity of extreme-event is critical. To implement this in practice, we introduce a flexible error-tolerance framework that applies a predefined threshold across all indices and adapts truncation levels by region and season, enabling substantial storage savings while safeguarding the integrity of climate diagnostics.