RNA extraction is essential for the molecular detection of common viral pathogens. However, available extraction methods and the need for ultra-cold storage limit molecular testing in resource-constrained settings. Herein, we describe the development of an economical RNAExtraction and Storage (RNAES) protocol that eliminates requirements for instrumentation, expensive materials, and preserved cold chain. Through an iterative process, we optimized viral lysis and RNA binding to and elution from glass fiber membranes included in simple RNAES packets. Efficient viral lysis was achieved with a nontoxic buffer containing sucrose, KCl, proteinase K, and carrier RNA. Viral RNA binding to glass fiber membranes was concentration dependent across seven orders of magnitude (4.0-10.0 log10 copies/μL) and significantly increased with an acidic arginine binding buffer. For the clinical evaluation, 36 dengue virus (DENV)-positive serum samples were extracted in duplicate with the optimized RNAES protocol and once in an EMAG instrument (bioMérieux). DENV RNA was successfully extracted from 71/72 replicates (98.6%) in the RNAES protocol, and real-time RT-PCR cycle threshold (CT) values correlated between extraction methods. DENV RNA, extracted from clinical samples, was stable when stored on dried RNAES membranes at ambient temperature for up to 35 days, with median eluate RNA concentration decreasing by 0.18 and 0.29 log10 copies/μL between day 0 and days 7 and 35, respectively. At a cost of $0.08/sample, RNAES packets address key limitations to available protocols and may increase capacity for molecular detection of RNA viruses. IMPORTANCE RNA extraction methods and ultra-cold storage requirements limit molecular testing for common viruses. We developed a simple, flexible, and economical method that simultaneously addresses these limitations. At $0.08/sample, the new RNAExtraction and Storage (RNAES) protocol successfully extracted viral RNA from acute-phase sera and provided stable, ambient-temperature RNA storage for 35 days. Using this approach, we expect to improve RNA virus detection and outbreak response in resource-constrained settings.