Aviation contributes significantly to climate change through CO₂ emissions and non-CO₂ effects such as contrail cirrus and ozone formation. As the latter effects depend strongly on location and time of emission, non-CO₂ impacts could bemitigated through optimized routing. Climate Change Functions (CCFs) and algorithmic Climate Change Functions (aCCFs) provide spatially and temporally resolved information on the effect of aviation emissions on the atmosphere, which enable the planning of such eco-efficient flight routes. While CCFs are computationally demanding, aCCFs offer simplified but faster estimates based on correlations with meteorological data, facilitating climate-optimized flight planning applications. As the current applicability of aCCFs is limited to specific regions and seasons according to previously available CCF calculations, this study aims to address these limitations by expanding the spatial and temporal scope of CCFs and by comparing results with existing aCCFs beyond their original temporal and spatial domain. Dedicated contrail and chemistry simulations were accomplished by means of a Lagrangian approach within the ECHAM/MESSy Atmospheric Chemistry (EMAC) climate model to calculate CCFs for a new date and new regions. This study advances aviation non-CO₂ climate impact modelling by expanding CCFs to U.S. and European airspaces, to a novel season, enhanced spatial and temporal resolution of contrail effects, refining ozone radiative forcing estimates, and incorporating long-term climate responses over a 100-year time horizon. The new CCFs show consistent magnitudes and spatial gradients with earlier CCFs, but reveal systematic underestimation of contrail radiative forcing due to low optical depths. The comparison of CCFs of the present study with aCCFs outside their design region and season indicates that aCCFs capture general magnitudes and most gradients but underestimate their variability, particularly for contrails and NO_x-induced effects, and reveals limitations at certain altitudes and seasons. While aCCFs offer a fast alternative for trajectory planning, they simplify complex processes compared to detailed CCF simulations. The comprehensive model setup presented in this study describes a pathway how further refine aCCF formulations and how to expand datasets to improve accuracy and applicability outside their original domain. The new CCFs from this study expand spatial (EU and continental US) and seasonal coverage (spring) and provide valuable data to advance future aCCF formulations for broader applications.