## 1 Introduction  
Continuous meteorological data records are required for the study of various natural phenomena, including climate change [1, 2, 3, 4], extreme weather events [5, 6, 7], ecosystem-atmosphere exchange [8, 9, 10], biochemical processes in peat [11], hydrology [12], and microbiology [13]. Severe weather conditions, power supply problems, instrument malfunction, or maintenance operations result in logging stoppage of meteorological variables and the appearance of gaps in the time series [14, 15]. The availability of hydrometeorological data is limited in northern latitudes because of a sparse monitoring network, harsh weather, and the high cost of experiments and instrument maintenance in these environments [16, 17]. Weather stations located directly in peatland areas are unique and rare, while peatlands are characterized by a specific local climate [16, 17, 18, 19]. A range of methods of gap-filling in the meteorological time series are used, e.g., simple arithmetic mean, regional weighting method [20], regression model [15, 21, 22], artificial neural network [23, 24, 25], machine learning [26, 27, 28, 29, 30], process parametrization model [31, 32],look-up tables [33; 34], downscaling of continuous dataset [35; 36; 37], etc. [PERSON] et al. [15] present an overview of many of these methods. The statistical downscaling approach [37] is the building of an empirical link between the large-scale and local-scale climate, identifying and applying a statistical model to climate model output. The downscaling of meteorological variables can be applied for gap-filling of local climate data. The downscaling of global reanalysis data allowed us to obtain local or high-resolution gridded data for various meteorological variables [21; 22; 38; 39; 40]. The Coordinated Downscaling Experiment (CORDEX) [41] of the World Climate Research Programme resulted in a vast number of bias-corrected national and global climate change projections. Statistical downscaling with varying levels of complexity were developed to correct the reanalysis and global climate model simulations [37; 38]. Distribution mapping or quantile mapping is a distribution-based approach that corrects the mean and variance together with wet day frequencies and intensities [22]. The combining of dynamic downscaling with convolution neural network [39] allowed us to obtain long-term high-resolution precipitation datasets in complex-terrain areas of the Tibetan Plateau. Filling the gaps in meteorological data measured at site level using the corrected continuous data available globally was developed by [35], and it showed good performance except for the wind speed field. The gap-filling methods based on reference time series from a neighboring weather station's products tested by [20] had good performance for gap-filling meteorological datasets with less than 40% of values missing. The use of a reference series obtained from remote sensing reduces the probability of unfilled gaps. Gap-filling of soil moisture data for Southern Europe [42] using linear, cubic, and autoregressive interpolation and support vector machine methods showed a good ability of the model to estimate soil moisture values in the temporal, but not spatial domain. Adaptation of generalized linear models to jointly simulate several meteorological variables (air temperature, relative air humidity, global radiation, and wind speed) on a sub-daily scale was successful for surface water stress estimation in central Tunisia [43]. The various gap-filling methods are widely applied for a time series of eddy-covariance data processing [31; 32; 33; 44; 44], where gaps could typically account for 20-60% of an annual half-hourly/hourly ET dataset [45]. The look-up tables and analysis of diurnal variations of the previous periods were recommended [33] as a standard technique for gap-filling of ecosystem fluxes for EUROFLUX [8] and AmeriFlux eddy covariance databases.