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.