As the terrestrial carbon sink remains the most uncertain component of the global CO₂ budget, systematic misrepresentation of biospheric CO₂ exchange in complex mountainous regions limits the reliability of climate projections. This study employs the Vegetation Photosynthesis and Respiration Model coupled to the Weather Research and Forecasting model (WRF-VPRM) in real-case simulations over the European Alps. It investigates whether Alpine CO₂ exchange is appropriately represented when using default or regionally optimized VPRM parameters, quantifies the sensitivity of modelled CO₂ exchange to horizontal grid spacing at scales typical for global weather prediction (9 km) and climate models (54 km), and identifies the physical drivers of resolution-induced biases. Simulations with coarser horizontal grid spacing are compared with a regional-scale 1 km reference. Throughout 2012, 12 clear-sky and 12 cloudy/rainy days are simulated using three different VPRM parameter sets: default European (DF), Alpine-optimized (ALPS), and site-specific (SITE).</p> <p>Validation against five Alpine FLUXNET sites indicates that the SITE parameters perform best overall. The ALPS configuration provides a nearly unbiased representation of ecosystem respiration (R_eco) but overestimates gross primary production (GPP), whereas the DF configuration strongly underestimates both R_eco and GPP. In DF, these biases partially compensate, resulting in comparatively good performance for net ecosystem exchange (NEE) despite physically inconsistent flux components.</p> <p>Systematic biases in CO₂ uptake and their magnitude depend on grid spacing and prevailing meteorological conditions. Resolution-induced biases in NEE (relative to 1 km simulations) under clear-sky conditions decrease from several percent (7 % for ALPS, 4 % for DF) at 9 km to near zero at 54 km. For clear sky, coarser resolutions yield higher net CO₂ uptake. In contrast, under cloudy and rainy conditions coarse grids have lower simulated uptake than at 1 km, while the biases substantially increase (from order 10 % at 9 km grid spacing to over 40 % at 54 km). If yearly NEE is estimated from 12 days each for clear-sky and cloudy/rainy conditions, differences due to resolution are minimal at 9 km , while differences due to the parameter set (ALPS vs. DF) amount to 15 %. At 54 km grid spacing, resolution effects for both ALPS (17 %) and DF (13 %) exceed parameter effects (8 %). Taken together, the results imply that resolution-induced errors govern annual NEE uncertainty at coarse resolution (O(100 km)), but at finer resolutions (O(10 km)) the relative impact of parameter optimization dominates.</p> <p>Analytical estimates based on temperature derivatives indicate that 35&ndash;42 % of the differences in GPP and 71&ndash;85 % in R_eco differences between resolutions can be attributed directly to temperature. Additionally, a linear perturbation analysis confirms the key role of temperature in unresolved topography, while it clarifies that radiation accounts for most of the remaining GPP variance and that e.g. water stress and vegetation types from satellite data add smaller but systematic biases.