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Abstract Malaria remains a major public health challenge in Nigeria, and increasing climate variability poses substantial threats to recent gains in control. However, malaria transmission does not respond uniformly to climate drivers across epidemiological settings, highlighting the need to explore climate-malaria dynamics within heterogeneous contexts. This study examined the non-stationary temporal dynamics of malaria incidence and two key climatic drivers—rainfall and temperature—in Lagos and Zamfara states. These states were selected to represent heterogeneous transmission intensities, urbanisation and climatic regimes. Monthly malaria incidence and corresponding climate data (2015–2024) were analysed using wavelet-based model to characterise the non-stationary periodicities, quantify time-varying climate–malaria associations and identify time-dependent lead–lag relationships. Malaria incidence exhibited transient semi-annual, annual, and multi-annual cycles that were weak and temporally localized, despite persistent annual cycles in rainfall and temperature in Lagos. Cross-wavelet spectra revealed intermittent associations within the 8–16-month band, while phase analysis indicated short-lived alignment in which malaria incidence lagged rainfall by approximately one month, particularly between 2019 and 2022. The relationship with temperature was unstable, suggesting rainfall exerted more consistent influence on malaria incidence. In contrast, Zamfara displayed strong and dominant annual cycles of malaria incidence throughout the study period, with rainfall and temperature showing stable, statistically significant annual co-variability. Phase analysis revealed malaria incidence lagged rainfall by approximately one month and temperature by approximately three to four months, consistent with climate-modulated transmission processes. These findings highlight the heterogeneity of climate-malaria dynamics across transmission settings with contrasting epidemiological implications within Nigeria. The observed lag structures provide a basis for climate-informed early warning systems and intervention timing. While non-climatic drivers were not explicitly modelled, the analysis focuses on isolating climate-driven temporal signals. Consequently, to sustain control and elimination progress, climate-adaptive surveillance and region-specific interventions that anticipate rainfall- and temperature-driven transmission cycles must be integrated into Nigeria’s malaria control framework to ensure timely, targeted, and climate-resilient public health responses. Author summary Malaria transmission does not respond uniformly to climate drivers across epidemiological settings, highlighting the need to explore climate-malaria dynamics within heterogeneous contexts. Identical climatic forcing can produce qualitatively different outcomes depending on the underlying epidemiological setting, indicating the limitations of generalising control efforts from a single context. Motivated by the need to understand these differences, in this study, we examined the cross-epidemiological scale-dependent and lag-specific climatic forcing of malaria transmission at the sub-national context, providing support for malaria control and elimination strategies. We addressed the following questions to understand the hidden patterns of the temporal cycles and the corresponding associations between the climate variables and malaria incidence in the two states: What are the dominant temporal cycles in malaria incidence in the study region? How do the periodicities of climate variables compare with those of malaria incidence? Are there significant time-dependent associations between climate variability and malaria incidence? How do these association vary across different time scales (intra-annual vs interannual) and periods? What is the average lag between changes in key climate variables and malaria incidence? Monthly malaria incidence data and corresponding rainfall and temperature records spanning 2015–2024 were analysed using a continuous wavelet transform (CWT) framework. Scale-specific periodicities were identified using wavelet power spectra, while climate–malaria associations were quantified using cross-wavelet power and wavelet transform coherence (WTC). Phase difference analysis was employed to characterise time-varying lead–lag relationships between malaria incidence and climatic drivers at the annual timescale. Results show that in Lagos, malaria incidence is irregular and weakly linked to climate, reflecting the impact of interventions and socio-environmental factors that disrupt transmission. In contrast, Zamfara exhibits strong, regular annual cycles tightly coupled to rainfall and temperature, with malaria incidence lagging rainfall by about one month and temperature by three to four months. These findings highlight the need for region-specific strategies: sustaining intervention-driven disruption in low-burden urban areas, and intensifying climate-adaptive measures in high-burden rural settings. Integrating climate-sensitive surveillance and tailored intervention timing into Nigeria’s malaria control framework will strengthen resilience and accelerate progress toward elimination. Specifically, our findings demonstrate evidence-based framework to guide climate-adaptive intervention timing. In Zamfara state, extreme heat between March and May as shown in the temperature profile, may reduce use of LLINs, indicating that mass distribution before and during these periods, within same year, may be less effective. The start of rain comes with a cooling effect which may facilitates good weather condition that encourages LLIN utilization. Correspondingly, LLIN distribution campaigns conducted in June or July, prior to peak rainfall and peak malaria incidence typically observed between August and October, may enhance intervention effectiveness. Coupled with other climate-sensitive control interventions (for example, seasonal malaria chemo-prevention), such campaigns should be repeated at intervals of no more than three years, in alignment with the observed multi-annual cycles of malaria incidence, to effectively mask malaria risk in Zamafara state. This implementation strategy could be employed in other high transmission states of Nigeria to mitigate malaria risk.