Identification of ‘cut-points’ or thresholds of climate factors would play a crucial role in alerting risks of climate change and providing guidance to policymakers. This study investigated a ‘Climate Threshold’ for emergency hospital admissions of chronic lower respiratory diseases by using a distributed lag non-linear model (DLNM). We analysed a unique longitudinal dataset (10 years, 2000–2009) on emergency hospital admissions, climate, and pollution factors for the Greater London. Our study extends existing work on this topic by considering non-linearity, lag effects between climate factors and disease exposure within the DLNM model considering B-spline as smoothing technique. The final model also considered natural cubic splines of time since exposure and ‘day of the week’ as confounding factors. The results of DLNM indicated a significant improvement in model fitting compared to a typical GLM model. The final model identified the thresholds of several climate factors including: high temperature (Symbol27 °C), low relative humidity (Symbol 40%), high Pm10 level (Symbol70-μg/m3), low wind speed (Symbol 2 knots) and high rainfall (Symbol30 mm). Beyond the threshold values, a significantly higher number of emergency admissions due to lower respiratory problems would be expected within the following 2–3 days after the climate shift in the Greater London. The approach will be useful to initiate ‘region and disease specific’ climate mitigation plans. It will help identify spatial hot spots and the most sensitive areas and population due to climate change, and will eventually lead towards a diversified health warning system tailored to specific climate zones and populations.