## 5 Conclusions  
This study compared five models for gap-filling of in situ observed meteorological time series based on downscaling of continuous atmospheric reanalysis data. Eight target meteorological variables were sequentially simulated using linear regression, linear regression with interactions, the Gaussian process regression models with exponential and rational quadratic kernel, and bilayer artificial neural network. Regression and artificial neural network models have proven to be valuable tools for modelling of meteorological time series. The main advantage of these simple models is their limited data requirement and easy applicability. Complex weather and climate variability is described by large climatic models generating spatially-temporally-physically coupled meteorological variables (so called reanalysis data). Nonlinear transformations of reanalysis data based on models trained by in situ observations allowed us to approximate a specific local meteorological condition with reasonable accuracy.  
Comparison of various models has demonstrated that the Gaussian process regression model with an exponential kernel and bilayer artificial neural network shows the best performance in reproduction of meteorological time series with various temporal structures.  
Simulated meteorological time series have a high correlation coefficient and low normalized root-mean-squared error compared to reference in situ observed data.  
The developed approach can serve as a reference tool to fill data gaps in the local meteorological data. The model performance depends on the number of input variables, training subset, and the type of modelled variable. The presented approach will be used to reconstruct meteorological time series for the Mukhrino Field station located in a typical West Siberian peatland, where long-term monitoring of peatland ecological and carbon balance studies is organized.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13042646/s1](https://www.mdpi.com/article/10.3390/app13042646/s1)]([https://www.mdpi.com/article/10.3390/app13042646/s1](https://www.mdpi.com/article/10.3390/app13042646/s1)), Table S1. Linear correlation coefficients between target meteorological variables and input variables from ERA and PCA datasets, Table S2: Long-term (2010-2021) mean values of meteorological variables according to various models, Table S3. Standard deviation (2010-2021) values of meteorological variables according to various models.  
The models testing and validation was supported by the Ministry of Science and Higher Education of the Russian Federation, grant No. 075-15-2020-787 for implementation of Major scientific projects on priority areas of scientific and technological development (the project \"Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory\"). The meteorological station operation and development was supported with a grant from the Tyumen region Government in accordance with the program of the West Siberian Interregional Scientific and Educational Centre (National Project \"Nauka\"). The preliminary data processing was carried out within a grant from the Russian Scientific Foundation, No. 22-47-04408.  
Not applicable.  
The raw data discussed in this paper are available for download from Zenodo [[https://doi.org/10.5281/zenodo.4323024](https://doi.org/10.5281/zenodo.4323024)]([https://doi.org/10.5281/zenodo.4323024](https://doi.org/10.5281/zenodo.4323024)).  
Automated weather station and maintenance, accurate data collection in adverse conditions were provided by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Thanks to [PERSON] for the inspiring discussions of the neural network model training. Thanks to four anonymous reviewers for valuable recommendations and comments.  
The author declares no conflict of interest.