ESSDD

Earth System Science Data Discussions

ESSDD

Earth Syst. Sci. Data Discuss.

1866-3591

Göttingen, Germany

10.5194/essd-2026-679

PRECISi: A High-Resolution Daily Gridded Precipitation Dataset for Sicily (1951–2022) Derived from a Doubly Conditional Geostatistical Framework

Beikahmadi

Niloufar

¹ Francipane

Antonio

https://orcid.org/0000-0001-7811-8023

¹ Treppiedi

Dario

https://orcid.org/0000-0002-1359-1650

¹ Noto

Leonardo Valerio

https://orcid.org/0000-0002-3280-2898

Department of Engineering, University of Palermo, Palermo, Italy

21 09 2026

2026 1 29

2026

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://essd.copernicus.org/preprints/essd-2026-679/

The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-679/essd-2026-679.pdf

Long-term, high-resolution precipitation datasets are indispensable for understanding hydroclimatic variability and supporting water-resource and climate-impact studies. Yet, the reconstruction of spatially consistent rainfall fields over extended historical periods remains a major challenge, particularly in data-sparse Mediterranean regions characterized by complex terrain and pronounced precipitation heterogeneity. This study introduces the first long-term, high-resolution gridded precipitation dataset for Sicily, delivering a continuous historical reconstruction since 1951 from sparse gauge observations through a novel two-phase, doubly conditional spatial interpolation framework. Calibration was performed on a dense modern network of automated rain gauges; a Transfer-Informed Modelling strategy, whereby all geostatistical parameters calibrated on this high-density contemporary period are transferred without modification to epochs of substantially lower network density, ensures temporal coherence across the full historical record. A merged archive of historical stations was assembled from two independent networks following rigorous homogeneity assessment of inter-network spatial coherence prior to reconstruction. Three principal methodological innovations distinguish the framework: (i) a double classification of daily events by intermittency and magnitude into distinct hydrometeorological regimes having unique spatial correlation structures; (ii) a comparison of regime-specific intensity-modelling paradigms based on geostatistics or regression; and (iii) a contrast of binary masking against probabilistic hurdle-weighting for occurrence-conditional intensity. Leave-one-out cross-validation demonstrated high rainfall detection capability across all intermittency classes and established the consistent superiority of the geostatistical framework over its regression-kriging counterpart across all hydrometeorological regimes. The superior optimal framework was consequently selected for operational historical reconstruction. Validated against an independent monthly climatological benchmark over the full study period, the bias-corrected primary dataset reproduces the reference climatology with high fidelity, affirming its fitness for long-term hydroclimatic analysis across topographically complex Mediterranean terrain (data is available at <a href="https://doi.org/10.5281/zenodo.19228150">https://doi.org/10.5281/zenodo.19228150</a>; BEIKAHMADI et al., 2026).