## 1. Introduction

Mongolia has more than 130 sparsely located meteorological stations. This sparse station network limits the possibility of obtaining the quantity of the fine-resolution weather and climate information of Mongolia.

The general circulation model (GCM), focusing on the global scale with coarse horizontal resolution, can be used to produce and reproduce weather and climate information (Meehl, 1995). However, with the horizontal scale, regional and local details influenced by spatial heterogeneities in the regional physiography can be lost in the GCM simulation. GCMs are, therefore, inherently unable to represent local subgrid-scale features and dynamics, such as local topographical characteristics and processes. Nevertheless, various statistical downscaling techniques are available to convert GCM outputs into local variables, which are appropriate for applications since local meteorological conditions are largely related to large-scale meteorology. The most widely used statistical downscaling tools usually apply linear methods, such as local scaling, multiple linear regression, canonical correlation analysis and singular value decomposition (Conway *et al.*, 1996; Henrik *et al.*, 1999; Coulibaly *et al.*, 2005; Sun and Chen, 2012).

Dynamically sophisticated methods that convert GCM output into regional meteorological variables using reliable regional climate model (RCM) are usually referred to as dynamical downscaling techniques. The downscaling based on the RCM simulation with nesting system is used to attain relatively fine horizontal resolution information of the order of tens kilometres or less over the selected domain of interest (e.g., Gomboluudev *et al.*, 2005; Im *et al.*, 2008; Altangerel *et al.*, 2011).

Although RCM can provide weather and climate information on a fine-scale in the area of interest, the model result contains errors and biases related due to incompleteness of the current model and modelling technique, lack of understanding of the complex nature of the earth system (Lorenz, 1963), the uncertainties in initial conditions, model physics and parameterizations, etc.

Therefore, various statistical correction techniques based on Model Output Statistic (Wilks, 1995) are largely used to remove systematic and non-systematic biases in the model results using both linear (e.g., Ahn *et al.*, 2002; Wood *et al.*, 2004; Déqué *et al.*, 2007; Fischer and Schär, 2010; Amengual *et al.*, 2012) and nonlinear (e.g., Xu, 1999; Schoof and Pryor, 2001; Ahn *et al.*, 2012) methods.

Ahn *et al.* (2012) used dynamical downscaling and statistical correction to remove the systematic biases from simulated results by dividing them into mean and perturbation parts. A self-organizing map (SOM) was firstly used to correct the perturbation part of the temperature bias (Ahn *et al.*, 2012).

In this study, the distribution of monthly temperature in Mongolia was reconstructed using dynamical downscaling with a horizontal resolution of 20 × 20 km and a statistical correction method for the removal of systematic biases in the downscaled results by dividing them into mean and perturbation parts. The effect of temperature inversion frequently occurring over Mongolia during winter was also corrected using a temperature inversion correction method developed in this study.

In Section 'Data and method', we explain the data used and the study methods. Section 'Results' compares the observation and downscaled results with and without statistical correction. The discussions and conclusions are presented in Section 'Discussion and conclusions'.