RASCAL v1.0.0: An Open Source Tool for Climatological Time Series Reconstruction and Extension

Abstract. The reduction of in situ observations in recent decades poses a potential risk of losing crucial information in regions where local effects significantly shape their climatology. Reanalyses face challenges in examining climatologies with highly localized effects, particularly in regions with intricate orography. Empirical downscaling methods offer a cost-effective and easier to implement in new areas alternative to dynamic downscaling methods. This article introduces RASCAL, an open-source Python tool designed to address gaps in observational climate data, especially in regions with limited long-term data and significant local effects, such as mountainous areas. Employing an object-oriented programming style, RASCAL's methodology effectively links large-scale circulation patterns with local atmospheric features, using the analog method in combination with principal components analysis (PCA), outperforming reanalysis in conveying climatic characteristics. The package contains routines for preprocessing observations and reanalysis data, generating reconstructions using various methods, and evaluating the reconstruction's performance in reproducing the time series of observations, statistical properties, and relevant climatic indices. Its high modularity and flexibility allows fast and reproducible downscaling. The evaluations carried out in central Spain, near a mountainous area and an urbanized area, demonstrate that RASCAL performs better than the ERA20C and ERA20CM reanalysis in terms of R2, standard deviation, and bias. This is particularly evident in the reconstruction of monthly total precipitation. It is worth noting that RASCAL generates series with statistical properties, such as seasonality and daily distributions, that closely resemble observations, thus addressing the limitations of reanalysis biases. This addresses the limitations of reanalysis biases and confirms the potential of this method for conducting robust climate research. The adaptability of RASCAL to diverse scientific objectives is also highlighted. However, there are challenges to consider, such as the requirement for long-term data series and susceptibility to disruptions caused by changes in land use or urbanization processes. Despite these limitations, RASCAL's positive outcomes offer opportunities for comprehensive climate variability analyses and potential applications in downscaling short-term forecasts, seasonal predictions, and climate change scenarios. The Python code and the Jupyter Notebook for the reconstruction validation are publicly available as an open project.