A Novel Conditional Two-Phase Framework for High-Resolution Long-Term Precipitation Reconstruction: The Case of Sicily (1951–2025)

Reliable, high-resolution gridded precipitation data are nowadays indispensable for modern climate science, hydrological modeling, and engineering applications, particularly in the Mediterranean region, where sharp topographic gradients and convective dynamics drive significant spatial variability. This study presents the development of a new daily gridded precipitation dataset for Sicily at a 2-km resolution, spanning the period 1951–2025. To address the challenges of reconstructing physically plausible fields from sparse historical records, we propose a "Conditional Two-Phase Reconstruction" framework that explicitly separates rainfall occurrence from conditional magnitude.

The methodology integrates heterogeneous in-situ observational sources, merging long-term historical archives with a modern, high-density automated rain gauge network. A core innovation of this work lies in the transfer of spatial model structures and precipitation regime definitions learned from the short-term dense network to the data-scarce historical period.

The framework first models spatial intermittency (Phase I) using regime-specific Indicator Kriging to distinguish between widespread precipitation and localized convective events. Subsequently, for magnitude estimation (Phase II), the study evaluates and implements three competing approaches: Geostatistical interpolation, hybrid Regression-Kriging utilizing Generalized Additive Models (GAMs), and Machine Learning via Extreme Gradient Boosting (XGBoost). To capture non-linear atmospheric interactions, the reconstruction leverages static physiographic predictors alongside dynamic atmospheric covariates derived from ERA5 reanalysis data, including Convective Available Potential Energy (CAPE) and Vertical Integrated Moisture Flux Divergence (VIMFD). By stratifying events into hydrometeorological regimes based on spatial coverage and intensity, the proposed framework provides a transferable blueprint for climate reconstruction in complex orographic domains. Models’ performance is evaluated through comprehensive Leave-One-Out cross validation using uncertainty and prediction error metrics.