Non-parametric kernel-based estimation and simulation of precipitation amountNon-parametric kernel-based estimation and simulation of precipitation amount Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2023-12-212022enThe probability distribution of precipitation amount strongly depends on geography, climate zone, and time scale considered. Closed-form parametric probability distributions are not sufficiently flexible to provide accurate and universal models for precipitation amount over different time scales. This paper derives non-parametric estimates of the cumulative distribution function (CDF) of precipitation amount for wet periods. The CDF estimates are obtained by integrating the kernel density estimator leading to semi-explicit CDF expressions for different kernel functions. An adaptive plug-in bandwidth estimator (KCDE) is investigated, using both synthetic data sets and reanalysis precipitation data from the Mediterranean island of Crete (Greece). It is shown that KCDE provides better estimates of the probability distribution than the standard empirical (staircase) estimate and kernel-based estimates that use the normal reference bandwidth. It is also demonstrated that KCDE enables the simulation of non-parametric precipitation amount distributions by means of the inverse transform sampling method.This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014–2020” in the context of project “Gaussian Anamorphosis with Kernel Estimators for Spatially Distributed Data and Time Series and Applications in the Analysis of Precipitation” (MIS 5052133).http://creativecommons.org/licenses/by/4.0/Journal of Hydrology612 Pavlidis Andreas Παυλιδης Ανδρεας Agou Vasiliki Αγου Βασιλικη Christopoulos Dionysios Χριστοπουλος Διονυσιος Elsevier Precipitation Kernel estimation Simulation Non-Gaussian Reanalysis data Non-parametric estimate