| 21 Aug 2026
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.
General comments
This report presents a case for taking caution when using the longest number of yearly consecutive dry days (CDD) for analyzing drought behavior. Using a combination of radar and station rainfall observations and comparing them with ERA5 and kilometer-scale regional climate models, the authors make a convincing argument that the interpretation of CDD becomes different when measured at a higher resolution.
This discussion does open an important conversation about what CDD is actually measuring when used in hydro-climate studies. My opinion is that CDD has traditionally been used in studies using coarser-grid climate models where simulated precipitation occurrence is stretched across a larger grid cell, thus reducing spatial variability and leading to the drizzle bias as this study noted. This artificially reduces the noise in CDD, making it easier to analyze, and draw conclusions about broad precipitation patterns over a large geographic area. The analysis in Petrova et al. (2024) is a good example of using CDD at the CMIP GCM scale to identify broad regions where dry spells are projected to change in the future.
Kilometer-scale models are essentially simulating precipitation in a scale that feels more realistic to the average person on the ground. As this study highlights, kilometer-scale models can simulate rainfall patterns similar to radar measurements which is a tool we use to often perceive weather at the local scale. For example, imagine during a dry summer, you can watch a convective storm bring heavy rainfall to one half of your city while missing your home in the other half of the city, thus leaving your garden more yellow and parched compared to those who saw the rain. A coarse climate model would likely simulate that a light rain happened over the whole city and that the dry spell ended over your home while a kilometer-scale model would simulate that the dry spell continues for half the city. For you and your dry garden, the continued dry spell more accurately describes your situation.
The point of this long and confusing example is that CDD at a higher resolution is measuring something different than CDD at a lower resolution. At a higher resolution, CDD may become less reliable to convey average precipitation patterns over a particular location, but it does become better at expressing the spatial variability of precipitation in a larger region. Especially as a warmer climate is expected to make precipitation more spatially concentrated and more convective, studying the future change in CDD with a kilometer-scale model can be useful for expressing how spatial distribution of precipitation becomes more scattered in the future. A higher variability in CDD across a region conveys that precipitation becomes more scattered or stochastic which has real consequences for agriculture as rainfall becomes less reliable.
Thus, the current text states to recommend caution when using CDD for drought evaluation, however I feel the analysis is pointing towards a different conclusion that is centered around the resolution at which CDD is being analyzed. The lesson could be refined to say that the intention of using CDD depends on the context and deserves caution depending on which resolution is being used and which precipitation characteristic is being analyzed.
Overall, I feel this report is well written and the analysis is robust and timely. I would support publication after a revision of the main lesson and the following comments are addressed
Specific comments
Minor comments
Line 57: Please briefly define what Moran’s I is for the readers who are not familiar with this term.
Line 139: You can bolster your argument here by saying that other threshold dependent climate indices, like days above a certain temperature threshold, have a higher spatial autocorrelation and are less stochastic, even at a higher resolution. Thus, CDD is uniquely problematic given its high spatial variability when examined at a high resolution.