Abstract <p>Reliable forecasting of renewable energy generation is essential for informed energy policy and planning, particularly in countries where detailed monthly data is scarce or inconsistently reported. This study proposes a cross-continental SARIMA-based forecasting framework that disaggregates annual energy data into monthly estimates and produces robust future projections. The framework is applied to three countries with varying energy profiles and data richness: Cameroon (solar), Saudi Arabia (solar), and Canada (hydropower). The motivation stems from the need to support policy design and energy security assessments in regions with weak data infrastructures, where monthly granularity is crucial for grid planning, climate commitments, and investment decisions. The empirical methodology involves the construction of univariate SARIMA models tailored to each country's historical patterns, combined with a proportional monthly disaggregation technique to generate pseudo-monthly datasets. These models are statistically validated through RMSE analysis and residual diagnostics to ensure forecasting accuracy. Results show forecasting errors ranging from 0.25 GWh (Cameroon) to 22.36 GWh (Canada), with the SARIMA framework demonstrating strong performance across both stable and rapidly growing energy systems. Key findings reveal that the proposed method is especially effective in data-poor environments and offers high interpretability and ease of implementation for policymakers. The disaggregated and forecasted data can be directly utilized by national energy agencies for seasonal planning, capacity expansion, and international climate reporting. This study contributes a practical and scalable approach to enhance renewable energy intelligence in emerging economies and supports broader sustainability and energy access goals.</p> Graphical Abstract <p></p>