LESSONS report: Fine-scale representation of consecutive dry days

Abstract. The number of consecutive dry days (CDD) is a widely used metric for assessing meteorological drought. This study investigates spatial CDD patterns over Germany during the drought year of 2018, comparing a convection-permitting model (CPM) at 3 km resolution with ERA5 reanalysis at 31 km, gauge-based interpolated observations at 1 km, and radar measurements at 1 km. The analysis reveals a surprise: high-resolution CPM and radar data produce strikingly heterogeneous, “brushstroke-like” spatial structures that differ fundamentally from the smooth fields of reanalysis and gridded observations.

Our report confirms that these sharp spatial discontinuities are not computational artifacts but a physical consequence of the sensitivity to the fixed daily dryness threshold of the CDD metric. We find that the CPM can well reproduce these fine-scale patterns that are also detected by radar measurements, which is quantified by a spatial autocorrelogram and the radially averaged power spectral density.

While averaging CDD over multiple years smooths out the brushstroke patterns, such temporal averaging does not resolve the underlying shortcoming: multi-year CDD is built upon individual annual CDD values that are, as shown here, highly variable and strongly dataset-dependent. A statistic whose single-year realizations are dominated by threshold-crossing noise provides an unstable foundation for climatological analysis regardless of the aggregation period applied. As a lesson learnt, we recommend caution when applying CDD in any context — including drought monitoring, impact assessment, and climate model evaluation. This study reports on the surprising fine-scale spatial patterns and resulting shortcomings of the CDD metric and is therefore submitted as a LESSONS report, a paper category dedicated to documenting Limitations, Errors, Surprises, Shortcomings, and Opportunities for New Science.