AbstractThe signal for climate change effects can be abstruse; consequently, interpretations of evidence must avoid verisimilitude, or else misattribution of causality could compromise policy decisions. Examining climatic effects on wild animal population dynamics requires ability to trap, observe or photograph and to recapture study individuals consistently. In this regard, we use 19 years of data (1994–2012), detailing the life histories on 1179 individual European badgers over 3288 (re‐) trapping events, to test whether trapping efficiency was associated with season, weather variables (both contemporaneous and time lagged), body‐condition index (BCI) and trapping efficiency (TE).PCAfactor loadings demonstrated thatTEwas affected significantly by temperature and precipitation, as well as time lags in these variables. From multi‐model inference,BCIwas the principal driver ofTE, where badgers in good condition were less likely to be trapped. Our analyses exposed that this was enacted mechanistically via weather variables drivingBCI, affectingTE. Notably, the very conditions that militated for poor trapping success have been associated with actual survival and population abundance benefits in badgers. Using these findings to parameterize simulations, projecting best‐/worst‐case scenario weather conditions andBCIresulted in 8.6% ± 4.9SDdifference in seasonalTE, leading to a potential 55.0% population abundance under‐estimation under the worst‐case scenario; 38.6% over‐estimation under the best case. Interestingly, simulations revealed that while any single trapping session might prove misrepresentative of the true population abundance, due to weather effects, prolonging capture–mark–recapture studies under sub‐optimal conditions decreased the accuracy of population estimates significantly. We also use these projection scenarios to explore how weather could impact government‐led trapping of badgers in theUK, in relation toTBmanagement. We conclude that population monitoring must be calibrated against the likelihood that weather conditions could be altering trap success directly, and therefore biasing model design.