Data and R code to support Lotto Batista et al., 2024: [Modelling the impact of climate and the environment on the spatiotemporal dynamics of Lyme borreliosis in Germany](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4867544) The analyses were done using R version 4.4.1 (2024-06-14). ## Summary *Background*: Lyme borreliosis (LB) is a predominant vector-borne disease in Europe, with Germany reporting endemic circulation for at least the past two decades. Climatic and environmental conditions are key drivers of tick activity. Understanding the climatic and environmental factors driving LB dynamics can help devise decision-support tools to guide interventions and adaptation strategies. *Methods*: Using a Bayesian modelling framework, we assessed the delayed and nonlinear effects of climate variation and land use change on monthly LB case counts from the German national notification system at a district level from 2009 to 2022. We evaluated the predictive performance of our model and then predicted risk trends in states without ongoing notification. Last, we used the fitted risk function for maximum temperature to assess long-term trends in relative risk since the 1950s. *Findings*: Our analyses reveal that climate and environmental factors are significant drivers of LB cases in Germany. Nonlinear maximum temperatures, ranging between 10·5°C and 26·3°C two to four months prior, and relative humidity levels exceeding 78·8% one month prior, along with high Standardised Precipitation Evapotranspiration Index (SPEI-3) values, were associated with increased LB risk. The effect of relative humidity was only relevant in areas suitable for deer population, potentially linked to ticks’ survival. Moreover, although the effect of maximum temperature on LB risk followed a similar pattern between genders, the peak among women was significantly higher than in men. Predictions from our model identified significant increasing trends in Bremen and Lower Saxony, two states without case notification. We also observed an increasing trend in maximum-temperature related LB relative risk in all Federal States, with the largest percentage change in the period 2013-2022 in northern districts, compared to 1951-1970. *Interpretation*: Our study underscores the importance of climatic variables as drivers of LB risk in Germany. We identified optimal conditions that may be related to human exposure and tick survival and detected long-term trends nationwide, including areas without notification. This decision-support modelling framework emphasises the added value of expanding LB surveillance in Germany and in the rest of the continent to address the emerging infectious disease risks posed by climate change.