ABSTRACT With the growing use of kilometer‐scale atmospheric models in climate‐scale applications, remapping data from high‐resolution grids to coarser grids and performing statistical diagnostics have become increasingly routine. Precipitation data often exhibit strong spatial and temporal discontinuities, making the choice of remapping algorithm critical for maintaining certain properties after remapping. The choice among different remapping methods, in particular between conservative and nonconservative schemes, is closely tied to which aspect of precipitation one wishes to preserve most faithfully. This study first highlights the substantial differences between conservative and nonconservative remapping methods/tools in fine‐to‐coarse remapping. Because these methods are inherently designed to prioritize either area‐mean values or pointwise‐like values, they produce markedly different error characteristics in metrics such as mean precipitation amount, frequency, intensity, diurnal peak timing, and the probability distribution function (PDF) of precipitation frequency. These differences become progressively more pronounced as the target resolution decreases. Conservative remapping tends to increase the proportion of weak rainfall events, thereby substantially altering the PDF of the frequency–intensity relationship. Because of its strong smoothing effect, it may lead to apparently improved RMSE/PCC values, as compared with nonconservative remapping. Inverse‐distance‐weighted remapping, as a representative pointwise‐type nonconservative method, is not flux conserving but more effectively preserves location‐specific rainfall characteristics. This makes error metrics and frequency–intensity spectra less sensitive to spatial coarsening. It is recommended that kilometer‐scale model evaluations should explicitly account for the distinct characteristics of both remapping categories, in alignment with the specific research objectives.
Chen et al. (2026) studied this question.