High-Resolution Hourly Precipitation Grids Since 1961: A Novel Deterministic Approach

High-resolution precipitation data are critical for addressing a wide range of hydrological and climatological challenges, including flood modelling, precipitation-runoff behaviour analyses, risk assessments, and climate trend studies. Despite their importance, no existing dataset combines the spatial resolution of 100 meters with hourly temporal resolution for large areas over extended historical periods. This gap severely limits the accuracy and applicability of current models and analyses. Our study introduces a innovative method to generate such datasets, covering the period from 1961 onward - the start of the modern climate reference period - thereby addressing this long-standing need.

The main challenge lies in the lack of suitable radar or station-based precipitation data before the early 2000s. While reanalysis products like ERA5 and ERA5-Land provide hourly data, their spatial resolutions (approximately 31 km and 9 km, respectively) are too coarse for applications requiring fine-scale insights. Existing disaggregation methods attempting to refine these datasets to higher resolutions often fall short in terms of consistency and accuracy. Our approach bridges this gap by combining machine learning techniques (gradient boosting decision trees), spatial interpolation methods, and meteorological station data at both hourly and daily resolutions.

The proposed deterministic method has been rigorously validated using spatial and temporal independent cross-validation with over 1,100 stations across Germany. The results demonstrate robust performance, achieving a correlation coefficient (R) exceeding 0.6 and a Heidke Skill Score (HSS) also above 0.6. These metrics underscore the method's reliability in accurately distinguishing between precipitation events and non-events while capturing precipitation intensity variations.

As a proof of concept, we applied our method to generate a high-resolution precipitation dataset for Saxony, Germany, which is freely available for analysis and application. This dataset represents a significant advancement in hydrological and climatological research tools, enabling unprecedented precision in modelling and analysis across diverse domains.