We report a 24-month statistical baseline climatology for continuously-measured atmospheric carbon dioxide (CO2) and methane (CH4) mixing ratios linked to surface meteorology as part of a wider environmental baselining project tasked with understanding pre-existing local environmental conditions prior to shale gas exploration in the United Kingdom. The baseline was designed to statistically characterise high-precision measurements of atmospheric composition gathered over two full years (between February 1st 2016 and January 31st 2018) at fixed ground-based measurement stations on, or near to, two UK sites being developed for shale gas exploration involving hydraulic fracturing. The sites, near Blackpool (Lancashire) and Kirby Misperton (North Yorkshire), were the first sites approved in the UK for shale gas exploration since a moratorium was lifted in England. The sites are operated by Cuadrilla Resources Ltd. and Third Energy Ltd., respectively. A statistical climatology of greenhouse gas mixing ratios linked to prevailing local surface meteorology is presented. This study diagnoses and interprets diurnal, day-of-week, and seasonal trends in measured mixing ratios and the contributory role of local, regional and long-range emission sources. The baseline provides a set of contextual statistical quantities against which the incremental impacts of new activities (in this case, future shale gas exploration) can be quantitatively assessed. The dataset may also serve to inform the design of future case studies, as well as direct baseline monitoring design at other potential shale gas and industrial sites. In addition, it provides a quantitative reference for future analyses of the impact, and efficacy, of specific policy interventions or mitigating practices. For example, statistically significant excursions in measured concentrations from this baseline (e.g. >99th percentile) observed during phases of operational extraction may be used to trigger further examination in order to diagnose the source(s) of emission and links to on-site activities at the time, which may be of importance to regulators, site operators and public health stakeholders. A guideline algorithm for identifying these statistically significant excursions, or "baseline deviation events", from the expected baseline conditions is presented and tested. Gaussian plume modelling is used to further these analyses, by simulating approximate upper-limits of CH4 fluxes which could be expected to give observable enhancements at the monitoring stations under defined meteorological conditions.