Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection-Permitting Regional Climate Model Simulations in a Complex Topographical Region

In mountainous areas, accurately estimating the long-term climatology of seasonal precipitations is challenging due to the lack of high-altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982-2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection-Permitting Regional Climate Model (CP-RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long-term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross-validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long-term seasonal precipitation from observations and CP-RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP-RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude-precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP-RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.